We found {len(matches)} new cat{'s' if len(matches) != 1 else ''} matching your preferences!
+{greeting}!
+Thank you for signing up for cat adoption alerts. We're excited to help you find your perfect feline companion!
+We'll notify you when new cats matching your preferences become available for adoption.
+What happens next?
+Happy cat hunting! ๐พ
+In loving memory of Kyra
+โ Error extracting profile
", "{}" + + profile = CatProfile(**profile_result["profile"]) + else: + # LOCAL: Use local agent + conversation = [{"role": "user", "content": user_input}] + profile = profile_agent.extract_profile(conversation) + + current_profile = profile + + # Perform search + response_msg = f"โ Got it! Searching for:\n\n" + \ + f"๐ Location: {profile.user_location or 'Not specified'}\n" + \ + f"๐ Distance: {profile.max_distance or 100} miles\n" + \ + f"๐จ Colors: {', '.join(profile.color_preferences) if profile.color_preferences else 'Any'}\n" + \ + f"๐ญ Personality: {profile.personality_description or 'Any'}\n" + \ + f"๐ Age: {', '.join(profile.age_range) if profile.age_range else 'Any'}\n" + \ + f"๐ถ Good with children: {'Yes' if profile.good_with_children else 'Not required'}\n" + \ + f"๐ Good with dogs: {'Yes' if profile.good_with_dogs else 'Not required'}\n" + \ + f"๐ฑ Good with cats: {'Yes' if profile.good_with_cats else 'Not required'}\n\n" + \ + f"Searching..." + + chat_history[1]["content"] = response_msg + + # Run search (Modal or local) + if is_production(): + # PRODUCTION: Call Modal API + import modal + + # Look up deployed function + search_cats_func = modal.Function.from_name("tuxedo-link-api", "search_cats") + + print("[INFO] Calling Modal API to search cats...") + search_result = search_cats_func.remote(profile.model_dump(), use_cache=use_cache) + + if not search_result["success"]: + error_msg = search_result.get('error', 'Unknown error') + chat_history.append({"role": "assistant", "content": f"โ Search error: {error_msg}"}) + return chat_history, "๐ฟ Search failed. Please try again.
", profile.json() + + # Reconstruct matches from Modal response + from models.cats import Cat + current_matches = [ + CatMatch( + cat=Cat(**m["cat"]), + match_score=m["match_score"], + vector_similarity=m["vector_similarity"], + attribute_match_score=m["attribute_match_score"], + explanation=m["explanation"], + matching_attributes=m.get("matching_attributes", []), + missing_attributes=m.get("missing_attributes", []) + ) + for m in search_result["matches"] + ] + else: + # LOCAL: Use local framework + result = framework.search(profile, use_cache=use_cache) + current_matches = result.matches + + # Build results HTML + if current_matches: + chat_history[1]["content"] += f"\n\nโจ Found {len(current_matches)} great matches!" + results_html = build_results_grid(current_matches) + else: + chat_history[1]["content"] += "\n\n๐ฟ No matches found. Try broadening your search criteria." + results_html = "No matches found
" + + # Profile JSON for display + profile_json = profile.model_dump_json(indent=2) + + return chat_history, results_html, profile_json + + except Exception as e: + error_msg = f"โ Error: {str(e)}" + print(f"[ERROR] Search failed: {e}") + import traceback + traceback.print_exc() + return [ + {"role": "user", "content": user_input}, + {"role": "assistant", "content": error_msg} + ], "Error occurred
", "{}" + + +def build_results_grid(matches: List[CatMatch]) -> str: + """Build HTML grid of cat results.""" + html = "Enter your preferences above to start searching
") + + # Profile display (collapsible) + with gr.Accordion("๐ Extracted Profile (for debugging)", open=False): + profile_display = gr.JSON(label="Profile Data") + + # Wire up events + submit_btn.click( + fn=extract_profile_from_text, + inputs=[user_input, use_cache_checkbox], + outputs=[chatbot, results_html, profile_display] + ) + + user_input.submit( + fn=extract_profile_from_text, + inputs=[user_input, use_cache_checkbox], + outputs=[chatbot, results_html, profile_display] + ) + + clear_btn.click( + fn=lambda: ([], "Enter your preferences above to start searching
", ""), + outputs=[chatbot, results_html, profile_display] + ) + + # Example buttons + examples = [ + "I want a friendly family cat in zip code 10001, good with children and dogs", + "Looking for a playful young kitten near New York City", + "I need a calm, affectionate adult cat that likes to cuddle", + "Show me cats good with children in the NYC area" + ] + + for btn, example in zip(example_btns, examples): + btn.click( + fn=search_with_examples, + inputs=[gr.State(example), use_cache_checkbox], + outputs=[chatbot, results_html, profile_display] + ) + + +def build_alerts_tab() -> None: + """Build the alerts management tab for scheduling email notifications.""" + with gr.Column(): + gr.Markdown("# ๐ Manage Alerts") + gr.Markdown("Save your search and get notified when new matching cats are available!") + + # Instructions + gr.Markdown(""" + ### How it works: + 1. **Search** for cats using your preferred criteria in the Search tab + 2. **Enter your email** below and choose notification frequency + 3. **Save Alert** to start receiving notifications + + You'll be notified when new cats matching your preferences become available! + """) + + # Save Alert Section + gr.Markdown("### ๐พ Save Current Search as Alert") + + with gr.Row(): + with gr.Column(scale=2): + email_input = gr.Textbox( + label="Email Address", + placeholder="your@email.com", + info="Where should we send notifications?" + ) + with gr.Column(scale=1): + frequency_dropdown = gr.Dropdown( + label="Notification Frequency", + choices=["Immediately", "Daily", "Weekly"], + value="Daily", + info="How often to check for new matches" + ) + + with gr.Row(): + save_btn = gr.Button("๐พ Save Alert", variant="primary", scale=2) + profile_display = gr.JSON( + label="Current Search Profile", + value={}, + visible=False, + scale=1 + ) + + save_status = gr.Markdown("") + + gr.Markdown("---") + + # Manage Alerts Section + gr.Markdown("### ๐ Your Saved Alerts") + + with gr.Row(): + with gr.Column(scale=2): + email_filter_input = gr.Textbox( + label="Filter by Email (optional)", + placeholder="your@email.com" + ) + with gr.Column(scale=1): + refresh_btn = gr.Button("๐ Refresh", size="sm") + + alerts_table = gr.Dataframe( + value=[], # Start empty - load on demand to avoid blocking UI startup + headers=["ID", "Email", "Frequency", "Location", "Preferences", "Last Sent", "Status"], + datatype=["number", "str", "str", "str", "str", "str", "str"], + interactive=False, + wrap=True + ) + + # Alert Actions + gr.Markdown("### โ๏ธ Manage Alert") + with gr.Row(): + alert_id_input = gr.Textbox( + label="Alert ID", + placeholder="Enter Alert ID from table above", + scale=2 + ) + with gr.Column(scale=3): + with gr.Row(): + toggle_btn = gr.Button("๐ Toggle Active/Inactive", size="sm") + delete_btn = gr.Button("๐๏ธ Delete Alert", variant="stop", size="sm") + + action_status = gr.Markdown("") + + # Wire up events + save_btn.click( + fn=save_alert, + inputs=[email_input, frequency_dropdown, profile_display], + outputs=[save_status, alerts_table] + ) + + refresh_btn.click( + fn=load_alerts, + inputs=[email_filter_input], + outputs=[alerts_table] + ) + + email_filter_input.submit( + fn=load_alerts, + inputs=[email_filter_input], + outputs=[alerts_table] + ) + + toggle_btn.click( + fn=toggle_alert_status, + inputs=[alert_id_input, email_filter_input], + outputs=[action_status, alerts_table] + ) + + delete_btn.click( + fn=delete_alert, + inputs=[alert_id_input, email_filter_input], + outputs=[action_status, alerts_table] + ) + + +def build_about_tab() -> None: + """Build the about tab with Kyra's story and application info.""" + with gr.Column(): + gr.Markdown("# ๐ฉ About Tuxedo Link") + + gr.Markdown(""" + ## In Loving Memory of Kyra ๐ฑ + + This application is dedicated to **Kyra**, a beloved companion who brought joy, + comfort, and unconditional love to our lives. Kyra was more than just a catโ + he was family, a friend, and a constant source of happiness. + + ### The Inspiration + + Kyra Link was created to help others find their perfect feline companion, + just as Kyra found his way into our hearts. Every cat deserves a loving home, + and every person deserves the companionship of a wonderful cat like Kyra. + + ### The Technology + + This application uses AI and machine learning to match prospective + adopters with their ideal cat: + + - **Natural Language Processing**: Understand your preferences in plain English + - **Semantic Search**: Find cats based on personality, not just keywords + - **Multi-Source Aggregation**: Search across multiple adoption platforms + - **Smart Deduplication**: Remove duplicate listings using AI + - **Image Recognition**: Match cats visually using computer vision + - **Hybrid Matching**: Combine semantic understanding with structured filters + + ### Features + + โ **Multi-Platform Search**: Petfinder, RescueGroups + โ **AI-Powered Matching**: Semantic search with vector embeddings + โ **Smart Deduplication**: Name, description, and image similarity + โ **Personality Matching**: Find cats that match your lifestyle + โ **Location-Based**: Search near you with customizable radius + + ### Technical Stack + + - **Frontend**: Gradio + - **Backend**: Python with Modal serverless + - **LLMs**: OpenAI GPT-4 for profile extraction + - **Vector DB**: ChromaDB with SentenceTransformers + - **Image AI**: CLIP for visual similarity + - **APIs**: Petfinder, RescueGroups, SendGrid + - **Database**: SQLite for caching and user management + + ### Open Source + + Tuxedo Link is open source and built as part of the Andela LLM Engineering bootcamp. + Contributions and improvements are welcome! + + ### Acknowledgments + + - **Petfinder**: For their comprehensive pet adoption API + - **RescueGroups**: For connecting rescues with adopters + - **Andela**: For the LLM Engineering bootcamp + - **Kyra**: For inspiring this project and bringing so much joy ๐ + + --- + + *"In memory of Kyra, who taught us that home is wherever your cat is."* + + ๐พ **May every cat find their perfect home** ๐พ + """) + + # Add Kyra's picture + with gr.Row(): + with gr.Column(): + gr.Image( + value="assets/Kyra.png", + label="Kyra - Forever in our hearts ๐", + show_label=True, + container=True, + width=400, + height=400, + show_download_button=False, + show_share_button=False, + interactive=False + ) + + +def create_app() -> gr.Blocks: + """ + Create and configure the Gradio application. + + Returns: + Configured Gradio Blocks application + """ + with gr.Blocks( + title="Tuxedo Link - Find Your Perfect Cat", + theme=gr.themes.Soft() + ) as app: + gr.Markdown(""" ++ AI-Powered Cat Adoption Search +
+Great personality match
+#Test HTML
", + text="Test Text" + ) + + assert result is True + mock_post.assert_called_once() + + # Check request parameters + call_args = mock_post.call_args + assert call_args[1]['auth'] == ('api', 'test-api-key') + assert call_args[1]['data']['to'] == 'recipient@test.com' + assert call_args[1]['data']['subject'] == 'Test Subject' + + @patch('agents.email_providers.mailgun_provider.requests.post') + @patch.dict('os.environ', {'MAILGUN_API_KEY': 'test-api-key'}) + @patch('agents.email_providers.mailgun_provider.get_mailgun_config') + @patch('agents.email_providers.mailgun_provider.get_email_config') + def test_send_email_failure(self, mock_email_config, mock_mailgun_config, mock_post): + """Test email sending failure.""" + mock_mailgun_config.return_value = {'domain': 'test.mailgun.org'} + mock_email_config.return_value = { + 'from_name': 'Test App', + 'from_email': 'test@test.com' + } + + # Mock failed response + mock_response = MagicMock() + mock_response.status_code = 400 + mock_response.text = "Bad Request" + mock_post.return_value = mock_response + + provider = MailgunProvider() + result = provider.send_email( + to="recipient@test.com", + subject="Test", + html="Test
", + text="Test" + ) + + assert result is False + + @patch.dict('os.environ', {'MAILGUN_API_KEY': 'test-api-key'}) + @patch('agents.email_providers.mailgun_provider.get_mailgun_config') + @patch('agents.email_providers.mailgun_provider.get_email_config') + def test_get_provider_name(self, mock_email_config, mock_mailgun_config): + """Test provider name.""" + mock_mailgun_config.return_value = {'domain': 'test.mailgun.org'} + mock_email_config.return_value = { + 'from_name': 'Test', + 'from_email': 'test@test.com' + } + + provider = MailgunProvider() + assert provider.get_provider_name() == "mailgun" + + +class TestSendGridProvider: + """Tests for SendGrid email provider (stub).""" + + @patch.dict('os.environ', {'SENDGRID_API_KEY': 'test-api-key'}) + @patch('agents.email_providers.sendgrid_provider.get_email_config') + def test_init(self, mock_email_config): + """Test SendGrid provider initialization.""" + mock_email_config.return_value = { + 'from_name': 'Test App', + 'from_email': 'test@test.com' + } + + provider = SendGridProvider() + + assert provider.api_key == 'test-api-key' + assert provider.default_from_name == 'Test App' + assert provider.default_from_email == 'test@test.com' + + @patch.dict('os.environ', {'SENDGRID_API_KEY': 'test-api-key'}) + @patch('agents.email_providers.sendgrid_provider.get_email_config') + def test_send_email_stub(self, mock_email_config): + """Test that SendGrid stub always succeeds.""" + mock_email_config.return_value = { + 'from_name': 'Test', + 'from_email': 'test@test.com' + } + + provider = SendGridProvider() + result = provider.send_email( + to="test@test.com", + subject="Test", + html="Test
", + text="Test" + ) + + # Stub should always return True + assert result is True + + @patch.dict('os.environ', {'SENDGRID_API_KEY': 'test-api-key'}) + @patch('agents.email_providers.sendgrid_provider.get_email_config') + def test_get_provider_name(self, mock_email_config): + """Test provider name.""" + mock_email_config.return_value = { + 'from_name': 'Test', + 'from_email': 'test@test.com' + } + + provider = SendGridProvider() + assert provider.get_provider_name() == "sendgrid (stub)" + + +class TestEmailProviderFactory: + """Tests for email provider factory.""" + + @patch('agents.email_providers.factory.get_configured_provider') + @patch.dict('os.environ', {'MAILGUN_API_KEY': 'test-key'}) + @patch('agents.email_providers.mailgun_provider.get_mailgun_config') + @patch('agents.email_providers.mailgun_provider.get_email_config') + def test_get_mailgun_provider(self, mock_email_config, mock_mailgun_config, mock_get_configured): + """Test getting Mailgun provider.""" + mock_get_configured.return_value = 'mailgun' + mock_mailgun_config.return_value = {'domain': 'test.mailgun.org'} + mock_email_config.return_value = { + 'from_name': 'Test', + 'from_email': 'test@test.com' + } + + provider = get_email_provider() + + assert isinstance(provider, MailgunProvider) + + @patch('agents.email_providers.factory.get_configured_provider') + @patch.dict('os.environ', {}) + @patch('agents.email_providers.sendgrid_provider.get_email_config') + def test_get_sendgrid_provider(self, mock_email_config, mock_get_configured): + """Test getting SendGrid provider.""" + mock_get_configured.return_value = 'sendgrid' + mock_email_config.return_value = { + 'from_name': 'Test', + 'from_email': 'test@test.com' + } + + provider = get_email_provider() + + assert isinstance(provider, SendGridProvider) + + @patch('agents.email_providers.factory.get_configured_provider') + def test_unknown_provider(self, mock_get_configured): + """Test that unknown provider raises error.""" + mock_get_configured.return_value = 'unknown' + + with pytest.raises(ValueError, match="Unknown email provider"): + get_email_provider() + + @patch.dict('os.environ', {'MAILGUN_API_KEY': 'test-key'}) + @patch('agents.email_providers.mailgun_provider.get_mailgun_config') + @patch('agents.email_providers.mailgun_provider.get_email_config') + def test_explicit_provider_name(self, mock_email_config, mock_mailgun_config): + """Test explicitly specifying provider name.""" + mock_mailgun_config.return_value = {'domain': 'test.mailgun.org'} + mock_email_config.return_value = { + 'from_name': 'Test', + 'from_email': 'test@test.com' + } + + provider = get_email_provider('mailgun') + + assert isinstance(provider, MailgunProvider) + diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/tests/unit/test_metadata_vectordb.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/tests/unit/test_metadata_vectordb.py new file mode 100644 index 0000000..e596e4c --- /dev/null +++ b/week8/community_contributions/dkisselev-zz/tuxedo_link/tests/unit/test_metadata_vectordb.py @@ -0,0 +1,154 @@ +"""Unit tests for metadata vector database.""" + +import pytest +import tempfile +import shutil +from pathlib import Path + +from setup_metadata_vectordb import MetadataVectorDB + + +@pytest.fixture +def temp_vectordb(): + """Create a temporary metadata vector database.""" + temp_dir = tempfile.mkdtemp() + vectordb = MetadataVectorDB(persist_directory=temp_dir) + + yield vectordb + + # Cleanup + shutil.rmtree(temp_dir, ignore_errors=True) + + +class TestMetadataVectorDB: + """Tests for MetadataVectorDB class.""" + + def test_initialization(self, temp_vectordb): + """Test vector DB initializes correctly.""" + assert temp_vectordb is not None + assert temp_vectordb.colors_collection is not None + assert temp_vectordb.breeds_collection is not None + + def test_index_colors(self, temp_vectordb): + """Test indexing colors.""" + colors = ["Black", "White", "Black & White / Tuxedo", "Orange / Red"] + + temp_vectordb.index_colors(colors, source="petfinder") + + # Check indexed + stats = temp_vectordb.get_stats() + assert stats['colors_count'] == len(colors) + + # Should not re-index same source + temp_vectordb.index_colors(colors, source="petfinder") + stats = temp_vectordb.get_stats() + assert stats['colors_count'] == len(colors) # Should not double + + def test_index_breeds(self, temp_vectordb): + """Test indexing breeds.""" + breeds = ["Siamese", "Persian", "Maine Coon", "Bengal"] + + temp_vectordb.index_breeds(breeds, source="petfinder") + + # Check indexed + stats = temp_vectordb.get_stats() + assert stats['breeds_count'] == len(breeds) + + def test_search_color_exact(self, temp_vectordb): + """Test searching for exact color match.""" + colors = ["Black", "White", "Black & White / Tuxedo"] + temp_vectordb.index_colors(colors, source="petfinder") + + # Search for exact match + results = temp_vectordb.search_color("tuxedo", source_filter="petfinder") + + assert len(results) > 0 + assert results[0]['color'] == "Black & White / Tuxedo" + assert results[0]['similarity'] > 0.5 # Should be reasonable similarity + + def test_search_color_fuzzy(self, temp_vectordb): + """Test searching for color with typo.""" + colors = ["Black & White / Tuxedo", "Orange / Red", "Gray / Blue / Silver"] + temp_vectordb.index_colors(colors, source="petfinder") + + # Search with typo + results = temp_vectordb.search_color("tuxado", source_filter="petfinder") # typo: tuxado + + assert len(results) > 0 + # Should still find tuxedo + assert "Tuxedo" in results[0]['color'] or "tuxado" in results[0]['color'].lower() + + def test_search_breed_exact(self, temp_vectordb): + """Test searching for exact breed match.""" + breeds = ["Siamese", "Persian", "Maine Coon"] + temp_vectordb.index_breeds(breeds, source="petfinder") + + results = temp_vectordb.search_breed("siamese", source_filter="petfinder") + + assert len(results) > 0 + assert results[0]['breed'] == "Siamese" + assert results[0]['similarity'] > 0.9 # Should be very high for exact match + + def test_search_breed_fuzzy(self, temp_vectordb): + """Test searching for breed with typo.""" + breeds = ["Maine Coon", "Ragdoll", "British Shorthair"] + temp_vectordb.index_breeds(breeds, source="petfinder") + + # Typo: "main coon" instead of "Maine Coon" + results = temp_vectordb.search_breed("main coon", source_filter="petfinder") + + assert len(results) > 0 + assert "Maine" in results[0]['breed'] or "Coon" in results[0]['breed'] + + def test_multiple_sources(self, temp_vectordb): + """Test indexing from multiple sources.""" + petfinder_colors = ["Black", "White", "Tabby"] + rescuegroups_colors = ["Black", "Grey", "Calico"] + + temp_vectordb.index_colors(petfinder_colors, source="petfinder") + temp_vectordb.index_colors(rescuegroups_colors, source="rescuegroups") + + # Should have both indexed + stats = temp_vectordb.get_stats() + assert stats['colors_count'] == len(petfinder_colors) + len(rescuegroups_colors) + + # Search with source filter + results = temp_vectordb.search_color("black", source_filter="petfinder") + assert len(results) > 0 + assert results[0]['source'] == "petfinder" + + def test_empty_search(self, temp_vectordb): + """Test searching with empty string.""" + colors = ["Black", "White"] + temp_vectordb.index_colors(colors, source="petfinder") + + results = temp_vectordb.search_color("", source_filter="petfinder") + assert len(results) == 0 + + results = temp_vectordb.search_color(None, source_filter="petfinder") + assert len(results) == 0 + + def test_no_match(self, temp_vectordb): + """Test search that returns no good matches.""" + colors = ["Black", "White"] + temp_vectordb.index_colors(colors, source="petfinder") + + # Search for something very different + results = temp_vectordb.search_color("xyzabc123", source_filter="petfinder") + + # Will return something (nearest neighbor) but with low similarity + if len(results) > 0: + assert results[0]['similarity'] < 0.5 # Low similarity + + def test_n_results(self, temp_vectordb): + """Test returning multiple results.""" + colors = ["Black", "White", "Black & White / Tuxedo", "Gray / Blue / Silver"] + temp_vectordb.index_colors(colors, source="petfinder") + + # Get top 3 results + results = temp_vectordb.search_color("black", n_results=3, source_filter="petfinder") + + assert len(results) <= 3 + # First should be best match + assert "Black" in results[0]['color'] + diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/tests/unit/test_models.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/tests/unit/test_models.py new file mode 100644 index 0000000..6167fbb --- /dev/null +++ b/week8/community_contributions/dkisselev-zz/tuxedo_link/tests/unit/test_models.py @@ -0,0 +1,186 @@ +"""Fixed unit tests for data models.""" + +import pytest +from datetime import datetime +from models.cats import Cat, CatProfile, CatMatch, AdoptionAlert, SearchResult + + +class TestCat: + """Tests for Cat model.""" + + def test_cat_creation(self): + """Test basic cat creation.""" + cat = Cat( + id="12345", + name="Fluffy", + breed="Persian", + age="adult", + gender="female", + size="medium", + city="New York", + state="NY", + source="petfinder", + organization_name="Test Rescue", + url="https://example.com/cat/12345" + ) + + assert cat.name == "Fluffy" + assert cat.breed == "Persian" + assert cat.age == "adult" + assert cat.gender == "female" + assert cat.size == "medium" + assert cat.organization_name == "Test Rescue" + + def test_cat_with_optional_fields(self): + """Test cat with all optional fields.""" + cat = Cat( + id="12345", + name="Fluffy", + breed="Persian", + age="adult", + gender="female", + size="medium", + city="New York", + state="NY", + source="petfinder", + organization_name="Test Rescue", + url="https://example.com/cat/12345", + description="Very fluffy", + primary_photo="http://example.com/photo.jpg", + adoption_fee=150.00, + good_with_children=True, + good_with_dogs=False, + good_with_cats=True + ) + + assert cat.description == "Very fluffy" + assert cat.adoption_fee == 150.00 + assert cat.good_with_children is True + + def test_cat_from_json(self): + """Test cat deserialization from JSON.""" + json_data = """ + { + "id": "12345", + "name": "Fluffy", + "breed": "Persian", + "age": "adult", + "gender": "female", + "size": "medium", + "city": "New York", + "state": "NY", + "source": "petfinder", + "organization_name": "Test Rescue", + "url": "https://example.com/cat/12345" + } + """ + + cat = Cat.model_validate_json(json_data) + assert cat.name == "Fluffy" + assert cat.id == "12345" + + +class TestCatProfile: + """Tests for CatProfile model.""" + + def test_profile_creation_minimal(self): + """Test profile with minimal fields.""" + profile = CatProfile() + + assert profile.personality_description == "" # Defaults to empty string + assert profile.max_distance == 100 + assert profile.age_range is None # No default + + def test_profile_creation_full(self): + """Test profile with all fields.""" + profile = CatProfile( + user_location="10001", + max_distance=50, + personality_description="friendly and playful", + age_range=["young", "adult"], + size=["small", "medium"], + good_with_children=True, + good_with_dogs=True, + good_with_cats=False + ) + + assert profile.user_location == "10001" + assert profile.max_distance == 50 + assert "young" in profile.age_range + assert profile.good_with_children is True + + +class TestCatMatch: + """Tests for CatMatch model.""" + + def test_match_creation(self): + """Test match creation.""" + cat = Cat( + id="12345", + name="Fluffy", + breed="Persian", + age="adult", + gender="female", + size="medium", + city="New York", + state="NY", + source="petfinder", + organization_name="Test Rescue", + url="https://example.com/cat/12345" + ) + + match = CatMatch( + cat=cat, + match_score=0.85, + vector_similarity=0.9, + attribute_match_score=0.8, + explanation="Great personality match" + ) + + assert match.cat.name == "Fluffy" + assert match.match_score == 0.85 + assert "personality" in match.explanation + + +class TestAdoptionAlert: + """Tests for AdoptionAlert model.""" + + def test_alert_creation(self): + """Test alert creation.""" + cat_profile = CatProfile( + user_location="10001", + personality_description="friendly" + ) + + alert = AdoptionAlert( + user_id=1, + user_email="test@example.com", + profile=cat_profile, # Correct field name + frequency="daily" + ) + + assert alert.user_email == "test@example.com" + assert alert.frequency == "daily" + assert alert.active is True + + +class TestSearchResult: + """Tests for SearchResult model.""" + + def test_search_result_creation(self): + """Test search result creation.""" + profile = CatProfile(user_location="10001") + + result = SearchResult( + matches=[], + total_found=0, + search_profile=profile, + search_time=1.23, + sources_queried=["petfinder"], + duplicates_removed=0 + ) + + assert result.total_found == 0 + assert result.search_time == 1.23 + assert "petfinder" in result.sources_queried + diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/__init__.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/__init__.py new file mode 100644 index 0000000..adb70bc --- /dev/null +++ b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/__init__.py @@ -0,0 +1,37 @@ +"""Utility functions for Tuxedo Link.""" + +from .deduplication import ( + create_fingerprint, + calculate_levenshtein_similarity, + calculate_text_similarity, +) +from .image_utils import generate_image_embedding, calculate_image_similarity +from .log_utils import reformat +from .config import ( + get_config, + is_production, + get_db_path, + get_vectordb_path, + get_email_provider, + get_email_config, + get_mailgun_config, + reload_config, +) + +__all__ = [ + "create_fingerprint", + "calculate_levenshtein_similarity", + "calculate_text_similarity", + "generate_image_embedding", + "calculate_image_similarity", + "reformat", + "get_config", + "is_production", + "get_db_path", + "get_vectordb_path", + "get_email_provider", + "get_email_config", + "get_mailgun_config", + "reload_config", +] + diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/breed_mapping.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/breed_mapping.py new file mode 100644 index 0000000..57bd890 --- /dev/null +++ b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/breed_mapping.py @@ -0,0 +1,174 @@ +""" +Breed mapping utilities for cat APIs. + +Handles mapping user breed terms to valid API breed values +using dictionary lookups, vector search, and exact matching. +""" + +import logging +from typing import List, Optional, Dict + +# Mapping of common user terms to API breed values +# These are fuzzy/colloquial terms that users might type +USER_TERM_TO_API_BREED: Dict[str, List[str]] = { + # Common misspellings and variations + "main coon": ["Maine Coon"], + "maine": ["Maine Coon"], + "ragdol": ["Ragdoll"], + "siames": ["Siamese"], + "persian": ["Persian"], + "bengal": ["Bengal"], + "british shorthair": ["British Shorthair"], + "russian blue": ["Russian Blue"], + "sphynx": ["Sphynx"], + "sphinx": ["Sphynx"], + "american shorthair": ["American Shorthair"], + "scottish fold": ["Scottish Fold"], + "abyssinian": ["Abyssinian"], + "birman": ["Birman"], + "burmese": ["Burmese"], + "himalayan": ["Himalayan"], + "norwegian forest": ["Norwegian Forest Cat"], + "norwegian forest cat": ["Norwegian Forest Cat"], + "oriental": ["Oriental"], + "somali": ["Somali"], + "turkish angora": ["Turkish Angora"], + "turkish van": ["Turkish Van"], + + # Mixed breeds + "mixed": ["Mixed Breed", "Domestic Short Hair", "Domestic Medium Hair", "Domestic Long Hair"], + "mixed breed": ["Mixed Breed", "Domestic Short Hair", "Domestic Medium Hair", "Domestic Long Hair"], + "domestic": ["Domestic Short Hair", "Domestic Medium Hair", "Domestic Long Hair"], + "dsh": ["Domestic Short Hair"], + "dmh": ["Domestic Medium Hair"], + "dlh": ["Domestic Long Hair"], + "tabby": ["Domestic Short Hair"], # Tabby is a pattern, not a breed + "tuxedo": ["Domestic Short Hair"], # Tuxedo is a color, not a breed +} + + +def normalize_user_breeds( + user_breeds: List[str], + valid_api_breeds: List[str], + vectordb: Optional[object] = None, + source: str = "petfinder", + similarity_threshold: float = 0.7 +) -> List[str]: + """ + Normalize user breed preferences to valid API breed values. + + Uses 3-tier strategy: + 1. Dictionary lookup for common variations + 2. Vector DB semantic search for fuzzy matching + 3. Direct string matching as fallback + + Args: + user_breeds: List of breed terms provided by the user + valid_api_breeds: List of breeds actually accepted by the API + vectordb: Optional MetadataVectorDB instance for semantic search + source: API source (petfinder/rescuegroups) for vector filtering + similarity_threshold: Minimum similarity score (0-1) for vector matches + + Returns: + List of valid API breed strings + """ + if not user_breeds: + return [] + + normalized_breeds = set() + + for user_term in user_breeds: + if not user_term or not user_term.strip(): + continue + + user_term_lower = user_term.lower().strip() + matched = False + + # Tier 1: Dictionary lookup (instant, common variations) + if user_term_lower in USER_TERM_TO_API_BREED: + mapped_breeds = USER_TERM_TO_API_BREED[user_term_lower] + for mapped_breed in mapped_breeds: + if mapped_breed in valid_api_breeds: + normalized_breeds.add(mapped_breed) + matched = True + + if matched: + logging.info(f"๐ฏ Dictionary match: '{user_term}' โ {list(mapped_breeds)}") + continue + + # Tier 2: Vector DB semantic search (fuzzy matching, handles typos) + if vectordb: + try: + matches = vectordb.search_breed( + user_term, + n_results=1, + source_filter=source + ) + + if matches and matches[0]['similarity'] >= similarity_threshold: + best_match = matches[0]['breed'] + similarity = matches[0]['similarity'] + + if best_match in valid_api_breeds: + normalized_breeds.add(best_match) + logging.info( + f"๐ Vector match: '{user_term}' โ '{best_match}' " + f"(similarity: {similarity:.2f})" + ) + matched = True + continue + except Exception as e: + logging.warning(f"Vector search failed for breed '{user_term}': {e}") + + # Tier 3: Direct string matching (exact or substring) + if not matched: + # Try exact match (case-insensitive) + for valid_breed in valid_api_breeds: + if valid_breed.lower() == user_term_lower: + normalized_breeds.add(valid_breed) + logging.info(f"โ Exact match: '{user_term}' โ '{valid_breed}'") + matched = True + break + + # Try substring match if exact didn't work + if not matched: + for valid_breed in valid_api_breeds: + if user_term_lower in valid_breed.lower(): + normalized_breeds.add(valid_breed) + logging.info(f"โ Substring match: '{user_term}' โ '{valid_breed}'") + matched = True + + # Log if no match found + if not matched: + logging.warning( + f"โ ๏ธ No breed match found for '{user_term}'. " + f"User will see broader results." + ) + + result = list(normalized_breeds) + logging.info(f"Breed normalization complete: {user_breeds} โ {result}") + return result + + +def get_breed_suggestions(breed_term: str, valid_breeds: List[str], top_n: int = 5) -> List[str]: + """ + Get breed suggestions for autocomplete or error messages. + + Args: + breed_term: Partial or misspelled breed name + valid_breeds: List of valid API breed values + top_n: Number of suggestions to return + + Returns: + List of suggested breed names + """ + term_lower = breed_term.lower().strip() + suggestions = [] + + # Find breeds containing the term + for breed in valid_breeds: + if term_lower in breed.lower(): + suggestions.append(breed) + + return suggestions[:top_n] + diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/color_mapping.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/color_mapping.py new file mode 100644 index 0000000..0f832da --- /dev/null +++ b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/color_mapping.py @@ -0,0 +1,224 @@ +""" +Color mapping utilities for cat APIs. + +Handles mapping user color terms to valid API color values +using dictionary lookups, vector search, and exact matching. +""" + +import logging +from typing import List, Dict, Optional + +# Mapping of common user terms to Petfinder API color values +# Based on actual Petfinder API color list +USER_TERM_TO_API_COLOR: Dict[str, List[str]] = { + # Tuxedo/Bicolor patterns + "tuxedo": ["Black & White / Tuxedo"], + "black and white": ["Black & White / Tuxedo"], + "black & white": ["Black & White / Tuxedo"], + "bicolor": ["Black & White / Tuxedo"], # Most common bicolor + + # Solid colors + "black": ["Black"], + "white": ["White"], + + # Orange variations + "orange": ["Orange / Red"], + "red": ["Orange / Red"], + "ginger": ["Orange / Red"], + "orange and white": ["Orange & White"], + "orange & white": ["Orange & White"], + + # Gray variations + "gray": ["Gray / Blue / Silver"], + "grey": ["Gray / Blue / Silver"], + "silver": ["Gray / Blue / Silver"], + "blue": ["Gray / Blue / Silver"], + "gray and white": ["Gray & White"], + "grey and white": ["Gray & White"], + + # Brown/Chocolate + "brown": ["Brown / Chocolate"], + "chocolate": ["Brown / Chocolate"], + + # Cream/Ivory + "cream": ["Cream / Ivory"], + "ivory": ["Cream / Ivory"], + "buff": ["Buff / Tan / Fawn"], + "tan": ["Buff / Tan / Fawn"], + "fawn": ["Buff / Tan / Fawn"], + + # Patterns + "calico": ["Calico"], + "dilute calico": ["Dilute Calico"], + "tortoiseshell": ["Tortoiseshell"], + "tortie": ["Tortoiseshell"], + "dilute tortoiseshell": ["Dilute Tortoiseshell"], + "torbie": ["Torbie"], + + # Tabby patterns + "tabby": ["Tabby (Brown / Chocolate)", "Tabby (Gray / Blue / Silver)", "Tabby (Orange / Red)"], + "brown tabby": ["Tabby (Brown / Chocolate)"], + "gray tabby": ["Tabby (Gray / Blue / Silver)"], + "grey tabby": ["Tabby (Gray / Blue / Silver)"], + "orange tabby": ["Tabby (Orange / Red)"], + "red tabby": ["Tabby (Orange / Red)"], + "tiger": ["Tabby (Tiger Striped)"], + "tiger striped": ["Tabby (Tiger Striped)"], + "leopard": ["Tabby (Leopard / Spotted)"], + "spotted": ["Tabby (Leopard / Spotted)"], + + # Point colors (Siamese-type) + "blue point": ["Blue Point"], + "chocolate point": ["Chocolate Point"], + "cream point": ["Cream Point"], + "flame point": ["Flame Point"], + "lilac point": ["Lilac Point"], + "seal point": ["Seal Point"], + + # Other + "smoke": ["Smoke"], + "blue cream": ["Blue Cream"], +} + + +def normalize_user_colors( + user_colors: List[str], + valid_api_colors: List[str], + vectordb: Optional[object] = None, + source: str = "petfinder", + similarity_threshold: float = 0.7 +) -> List[str]: + """ + Normalize user color preferences to valid API color values. + + Uses 3-tier strategy: + 1. Dictionary lookup for common color terms + 2. Vector DB semantic search for fuzzy matching + 3. Direct string matching as fallback + + Args: + user_colors: List of color terms provided by the user + valid_api_colors: List of colors actually accepted by the API + vectordb: Optional MetadataVectorDB instance for semantic search + source: API source (petfinder/rescuegroups) for vector filtering + similarity_threshold: Minimum similarity score (0-1) for vector matches + + Returns: + List of valid API color strings + """ + if not user_colors: + return [] + + normalized_colors = set() + + for user_term in user_colors: + if not user_term or not user_term.strip(): + continue + + user_term_lower = user_term.lower().strip() + matched = False + + # Tier 1: Dictionary lookup (instant, common color terms) + if user_term_lower in USER_TERM_TO_API_COLOR: + mapped_colors = USER_TERM_TO_API_COLOR[user_term_lower] + for mapped_color in mapped_colors: + if mapped_color in valid_api_colors: + normalized_colors.add(mapped_color) + matched = True + + if matched: + logging.info(f"๐ฏ Dictionary match: '{user_term}' โ {list(mapped_colors)}") + continue + + # Tier 2: Vector DB semantic search (fuzzy matching, handles typos) + if vectordb: + try: + matches = vectordb.search_color( + user_term, + n_results=1, + source_filter=source + ) + + if matches and matches[0]['similarity'] >= similarity_threshold: + best_match = matches[0]['color'] + similarity = matches[0]['similarity'] + + if best_match in valid_api_colors: + normalized_colors.add(best_match) + logging.info( + f"๐ Vector match: '{user_term}' โ '{best_match}' " + f"(similarity: {similarity:.2f})" + ) + matched = True + continue + except Exception as e: + logging.warning(f"Vector search failed for color '{user_term}': {e}") + + # Tier 3: Direct string matching (exact or substring) + if not matched: + # Try exact match (case-insensitive) + for valid_color in valid_api_colors: + if valid_color.lower() == user_term_lower: + normalized_colors.add(valid_color) + logging.info(f"โ Exact match: '{user_term}' โ '{valid_color}'") + matched = True + break + + # Try substring match if exact didn't work + if not matched: + for valid_color in valid_api_colors: + if user_term_lower in valid_color.lower(): + normalized_colors.add(valid_color) + logging.info(f"โ Substring match: '{user_term}' โ '{valid_color}'") + matched = True + + # Log if no match found + if not matched: + logging.warning( + f"โ ๏ธ No color match found for '{user_term}'. " + f"User will see broader results." + ) + + result = list(normalized_colors) + logging.info(f"Color normalization complete: {user_colors} โ {result}") + return result + + +def get_color_suggestions(color_term: str, valid_colors: List[str], top_n: int = 5) -> List[str]: + """ + Get color suggestions for autocomplete or error messages. + + Args: + color_term: Partial or misspelled color name + valid_colors: List of valid API color values + top_n: Number of suggestions to return + + Returns: + List of suggested color names + """ + term_lower = color_term.lower().strip() + suggestions = [] + + # Find colors containing the term + for color in valid_colors: + if term_lower in color.lower(): + suggestions.append(color) + + return suggestions[:top_n] + + +def get_color_help_text(valid_colors: List[str]) -> str: + """ + Generate help text for valid colors. + + Args: + valid_colors: List of valid API colors + + Returns: + Formatted string describing valid colors + """ + if not valid_colors: + return "No color information available." + + return f"Valid colors: {', '.join(valid_colors)}" + diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/config.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/config.py new file mode 100644 index 0000000..55433e4 --- /dev/null +++ b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/config.py @@ -0,0 +1,134 @@ +"""Configuration management for Tuxedo Link.""" + +import yaml +import os +from pathlib import Path +from typing import Dict, Any + + +_config_cache: Dict[str, Any] = None + + +def load_config() -> Dict[str, Any]: + """ + Load configuration from YAML with environment variable overrides. + + Returns: + Dict[str, Any]: Configuration dictionary + """ + global _config_cache + if _config_cache: + return _config_cache + + # Determine config path - look for config.yaml, fallback to example + project_root = Path(__file__).parent.parent + config_path = project_root / "config.yaml" + + if not config_path.exists(): + config_path = project_root / "config.example.yaml" + + if not config_path.exists(): + raise FileNotFoundError( + "No config.yaml or config.example.yaml found. " + "Please copy config.example.yaml to config.yaml and configure it." + ) + + # Load YAML + with open(config_path) as f: + config = yaml.safe_load(f) + + # Override with environment variables if present + if 'EMAIL_PROVIDER' in os.environ: + config['email']['provider'] = os.environ['EMAIL_PROVIDER'] + if 'DEPLOYMENT_MODE' in os.environ: + config['deployment']['mode'] = os.environ['DEPLOYMENT_MODE'] + if 'MAILGUN_DOMAIN' in os.environ: + config['mailgun']['domain'] = os.environ['MAILGUN_DOMAIN'] + + _config_cache = config + return config + + +def get_config() -> Dict[str, Any]: + """ + Get current configuration. + + Returns: + Dict[str, Any]: Configuration dictionary + """ + return load_config() + + +def is_production() -> bool: + """ + Check if running in production mode. + + Returns: + bool: True if production mode, False if local + """ + return get_config()['deployment']['mode'] == 'production' + + +def get_db_path() -> str: + """ + Get database path based on deployment mode. + + Returns: + str: Path to database file + """ + config = get_config() + mode = config['deployment']['mode'] + return config['deployment'][mode]['db_path'] + + +def get_vectordb_path() -> str: + """ + Get vector database path based on deployment mode. + + Returns: + str: Path to vector database directory + """ + config = get_config() + mode = config['deployment']['mode'] + return config['deployment'][mode]['vectordb_path'] + + +def get_email_provider() -> str: + """ + Get configured email provider. + + Returns: + str: Email provider name (mailgun or sendgrid) + """ + return get_config()['email']['provider'] + + +def get_email_config() -> Dict[str, str]: + """ + Get email configuration. + + Returns: + Dict[str, str]: Email configuration (from_name, from_email) + """ + return get_config()['email'] + + +def get_mailgun_config() -> Dict[str, str]: + """ + Get Mailgun configuration. + + Returns: + Dict[str, str]: Mailgun configuration (domain) + """ + return get_config()['mailgun'] + + +def reload_config() -> None: + """ + Force reload configuration from file. + Useful for testing or when config changes. + """ + global _config_cache + _config_cache = None + load_config() + diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/deduplication.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/deduplication.py new file mode 100644 index 0000000..1766f70 --- /dev/null +++ b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/deduplication.py @@ -0,0 +1,201 @@ +"""Deduplication utilities for identifying duplicate cat listings.""" + +import hashlib +from typing import Tuple +import Levenshtein + +from models.cats import Cat + + +def create_fingerprint(cat: Cat) -> str: + """ + Create a fingerprint for a cat based on stable attributes. + + The fingerprint is a hash of: + - Organization name (normalized) + - Breed (normalized) + - Age + - Gender + + Args: + cat: Cat object + + Returns: + Fingerprint hash (16 characters) + """ + components = [ + cat.organization_name.lower().strip(), + cat.breed.lower().strip(), + str(cat.age).lower(), + cat.gender.lower() + ] + + # Create hash from combined components + combined = '|'.join(components) + hash_obj = hashlib.sha256(combined.encode()) + + # Return first 16 characters of hex digest + return hash_obj.hexdigest()[:16] + + +def calculate_levenshtein_similarity(str1: str, str2: str) -> float: + """ + Calculate normalized Levenshtein similarity between two strings. + + Similarity = 1 - (distance / max_length) + + Args: + str1: First string + str2: Second string + + Returns: + Similarity score (0-1, where 1 is identical) + """ + if not str1 or not str2: + return 0.0 + + # Normalize strings + str1 = str1.lower().strip() + str2 = str2.lower().strip() + + # Handle identical strings + if str1 == str2: + return 1.0 + + # Calculate Levenshtein distance + distance = Levenshtein.distance(str1, str2) + + # Normalize by maximum possible distance + max_length = max(len(str1), len(str2)) + + if max_length == 0: + return 1.0 + + similarity = 1.0 - (distance / max_length) + + return max(0.0, similarity) + + +def calculate_text_similarity(cat1: Cat, cat2: Cat) -> Tuple[float, float]: + """ + Calculate text similarity between two cats (name and description). + + Args: + cat1: First cat + cat2: Second cat + + Returns: + Tuple of (name_similarity, description_similarity) + """ + # Name similarity + name_similarity = calculate_levenshtein_similarity(cat1.name, cat2.name) + + # Description similarity + desc_similarity = calculate_levenshtein_similarity( + cat1.description, + cat2.description + ) + + return name_similarity, desc_similarity + + +def calculate_composite_score( + name_similarity: float, + description_similarity: float, + image_similarity: float, + name_weight: float = 0.4, + description_weight: float = 0.3, + image_weight: float = 0.3 +) -> float: + """ + Calculate a composite similarity score from multiple signals. + + Args: + name_similarity: Name similarity (0-1) + description_similarity: Description similarity (0-1) + image_similarity: Image similarity (0-1) + name_weight: Weight for name similarity + description_weight: Weight for description similarity + image_weight: Weight for image similarity + + Returns: + Composite score (0-1) + """ + # Ensure weights sum to 1 + total_weight = name_weight + description_weight + image_weight + if total_weight == 0: + return 0.0 + + # Normalize weights + name_weight /= total_weight + description_weight /= total_weight + image_weight /= total_weight + + # Calculate weighted score + score = ( + name_similarity * name_weight + + description_similarity * description_weight + + image_similarity * image_weight + ) + + return score + + +def normalize_string(s: str) -> str: + """ + Normalize a string for comparison. + + - Convert to lowercase + - Strip whitespace + - Remove extra spaces + + Args: + s: String to normalize + + Returns: + Normalized string + """ + import re + s = s.lower().strip() + s = re.sub(r'\s+', ' ', s) # Replace multiple spaces with single space + return s + + +def calculate_breed_similarity(breed1: str, breed2: str) -> float: + """ + Calculate breed similarity with special handling for mixed breeds. + + Args: + breed1: First breed + breed2: Second breed + + Returns: + Similarity score (0-1) + """ + breed1_norm = normalize_string(breed1) + breed2_norm = normalize_string(breed2) + + # Exact match + if breed1_norm == breed2_norm: + return 1.0 + + # Check if both are domestic shorthair/longhair (very common) + domestic_variants = ['domestic short hair', 'domestic shorthair', 'dsh', + 'domestic long hair', 'domestic longhair', 'dlh', + 'domestic medium hair', 'domestic mediumhair', 'dmh'] + + if breed1_norm in domestic_variants and breed2_norm in domestic_variants: + return 0.9 # High similarity for domestic cats + + # Check for mix/mixed keywords + mix_keywords = ['mix', 'mixed', 'tabby'] + breed1_has_mix = any(keyword in breed1_norm for keyword in mix_keywords) + breed2_has_mix = any(keyword in breed2_norm for keyword in mix_keywords) + + if breed1_has_mix and breed2_has_mix: + # Both are mixes, higher tolerance + return calculate_levenshtein_similarity(breed1, breed2) * 0.9 + + # Standard Levenshtein similarity + return calculate_levenshtein_similarity(breed1, breed2) + diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/geocoding.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/geocoding.py new file mode 100644 index 0000000..86db13b --- /dev/null +++ b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/geocoding.py @@ -0,0 +1,161 @@ +"""Geocoding utilities for location services.""" + +import requests +from typing import Optional, Tuple + + +def geocode_location(location: str) -> Optional[Tuple[float, float]]: + """ + Convert a location string (address, city, or ZIP) to latitude/longitude. + + Uses the free Nominatim API (OpenStreetMap). + + Args: + location: Location string (address, city, ZIP code, etc.) + + Returns: + Tuple of (latitude, longitude) or None if geocoding fails + """ + try: + # Use Nominatim API (free, no API key required) + url = "https://nominatim.openstreetmap.org/search" + params = { + 'q': location, + 'format': 'json', + 'limit': 1, + 'countrycodes': 'us,ca' # Limit to US and Canada + } + headers = { + 'User-Agent': 'TuxedoLink/1.0' # Required by Nominatim + } + + response = requests.get(url, params=params, headers=headers, timeout=10) + response.raise_for_status() + + results = response.json() + if results and len(results) > 0: + lat = float(results[0]['lat']) + lon = float(results[0]['lon']) + return lat, lon + + return None + + except Exception as e: + print(f"Geocoding failed for '{location}': {e}") + return None + + +def reverse_geocode(latitude: float, longitude: float) -> Optional[dict]: + """ + Convert latitude/longitude to address information. + + Args: + latitude: Latitude + longitude: Longitude + + Returns: + Dictionary with address components or None if failed + """ + try: + url = "https://nominatim.openstreetmap.org/reverse" + params = { + 'lat': latitude, + 'lon': longitude, + 'format': 'json' + } + headers = { + 'User-Agent': 'TuxedoLink/1.0' + } + + response = requests.get(url, params=params, headers=headers, timeout=10) + response.raise_for_status() + + result = response.json() + if 'address' in result: + address = result['address'] + return { + 'city': address.get('city', address.get('town', address.get('village', ''))), + 'state': address.get('state', ''), + 'zip': address.get('postcode', ''), + 'country': address.get('country', ''), + 'display_name': result.get('display_name', '') + } + + return None + + except Exception as e: + print(f"Reverse geocoding failed for ({latitude}, {longitude}): {e}") + return None + + +def calculate_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float: + """ + Calculate the great circle distance between two points in miles. + + Uses the Haversine formula. + + Args: + lat1: Latitude of first point + lon1: Longitude of first point + lat2: Latitude of second point + lon2: Longitude of second point + + Returns: + Distance in miles + """ + from math import radians, sin, cos, sqrt, atan2 + + # Earth's radius in miles + R = 3959.0 + + # Convert to radians + lat1_rad = radians(lat1) + lon1_rad = radians(lon1) + lat2_rad = radians(lat2) + lon2_rad = radians(lon2) + + # Differences + dlat = lat2_rad - lat1_rad + dlon = lon2_rad - lon1_rad + + # Haversine formula + a = sin(dlat/2)**2 + cos(lat1_rad) * cos(lat2_rad) * sin(dlon/2)**2 + c = 2 * atan2(sqrt(a), sqrt(1-a)) + + distance = R * c + + return distance + + +def parse_location_input(location_input: str) -> Optional[Tuple[float, float]]: + """ + Parse location input that might be coordinates or an address. + + Handles formats: + - "lat,long" (e.g., "40.7128,-74.0060") + - ZIP code (e.g., "10001") + - City, State (e.g., "New York, NY") + - Full address + + Args: + location_input: Location string + + Returns: + Tuple of (latitude, longitude) or None if parsing fails + """ + # Try to parse as coordinates first + if ',' in location_input: + parts = location_input.split(',') + if len(parts) == 2: + try: + lat = float(parts[0].strip()) + lon = float(parts[1].strip()) + # Basic validation + if -90 <= lat <= 90 and -180 <= lon <= 180: + return lat, lon + except ValueError: + pass # Not coordinates, try geocoding + + # Fall back to geocoding + return geocode_location(location_input) + diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/image_utils.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/image_utils.py new file mode 100644 index 0000000..db57595 --- /dev/null +++ b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/image_utils.py @@ -0,0 +1,168 @@ +"""Image utilities for generating and comparing image embeddings.""" + +import numpy as np +import requests +from PIL import Image +from io import BytesIO +from typing import Optional +import open_clip +import torch + + +class ImageEmbeddingGenerator: + """Generate image embeddings using CLIP model.""" + + def __init__(self, model_name: str = 'ViT-B-32', pretrained: str = 'openai'): + """ + Initialize the embedding generator. + + Args: + model_name: CLIP model architecture + pretrained: Pretrained weights to use + """ + self.device = "cuda" if torch.cuda.is_available() else "cpu" + self.model, _, self.preprocess = open_clip.create_model_and_transforms( + model_name, + pretrained=pretrained, + device=self.device + ) + self.model.eval() + + def download_image(self, url: str, timeout: int = 10) -> Optional[Image.Image]: + """ + Download an image from a URL. + + Args: + url: Image URL + timeout: Request timeout in seconds + + Returns: + PIL Image or None if download fails + """ + try: + response = requests.get(url, timeout=timeout) + response.raise_for_status() + img = Image.open(BytesIO(response.content)) + return img.convert('RGB') # Ensure RGB format + except Exception as e: + print(f"Failed to download image from {url}: {e}") + return None + + def generate_embedding(self, image: Image.Image) -> np.ndarray: + """ + Generate CLIP embedding for an image. + + Args: + image: PIL Image + + Returns: + Numpy array of image embedding + """ + with torch.no_grad(): + image_input = self.preprocess(image).unsqueeze(0).to(self.device) + image_features = self.model.encode_image(image_input) + + # Normalize embedding + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + + # Convert to numpy + embedding = image_features.cpu().numpy().flatten() + + return embedding.astype(np.float32) + + def generate_embedding_from_url(self, url: str) -> Optional[np.ndarray]: + """ + Download an image and generate its embedding. + + Args: + url: Image URL + + Returns: + Numpy array of image embedding or None if failed + """ + image = self.download_image(url) + if image is None: + return None + return self.generate_embedding(image) + + +# Global instance (lazy loaded) +_embedding_generator: Optional[ImageEmbeddingGenerator] = None + + +def get_embedding_generator() -> ImageEmbeddingGenerator: + """Get or create the global embedding generator instance.""" + global _embedding_generator + if _embedding_generator is None: + _embedding_generator = ImageEmbeddingGenerator() + return _embedding_generator + + +def generate_image_embedding(image_url: str) -> Optional[np.ndarray]: + """ + Generate an image embedding from a URL. + + This is a convenience function that uses the global embedding generator. + + Args: + image_url: URL of the image + + Returns: + Numpy array of image embedding or None if failed + """ + generator = get_embedding_generator() + return generator.generate_embedding_from_url(image_url) + + +def calculate_image_similarity(embedding1: np.ndarray, embedding2: np.ndarray) -> float: + """ + Calculate cosine similarity between two image embeddings. + + Args: + embedding1: First image embedding + embedding2: Second image embedding + + Returns: + Similarity score (0-1, where 1 is most similar) + """ + if embedding1 is None or embedding2 is None: + return 0.0 + + # Ensure embeddings are normalized + norm1 = np.linalg.norm(embedding1) + norm2 = np.linalg.norm(embedding2) + + if norm1 == 0 or norm2 == 0: + return 0.0 + + embedding1_norm = embedding1 / norm1 + embedding2_norm = embedding2 / norm2 + + # Cosine similarity + similarity = np.dot(embedding1_norm, embedding2_norm) + + # Clip to [0, 1] range (cosine similarity is [-1, 1]) + similarity = (similarity + 1) / 2 + + return float(similarity) + + +def batch_generate_embeddings(image_urls: list[str]) -> list[Optional[np.ndarray]]: + """ + Generate embeddings for multiple images. + + Args: + image_urls: List of image URLs + + Returns: + List of embeddings (same length as input, None for failed downloads) + """ + generator = get_embedding_generator() + embeddings = [] + + for url in image_urls: + embedding = generator.generate_embedding_from_url(url) + embeddings.append(embedding) + + return embeddings + diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/log_utils.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/log_utils.py new file mode 100644 index 0000000..68ddfac --- /dev/null +++ b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/log_utils.py @@ -0,0 +1,46 @@ +"""Logging utilities for Tuxedo Link.""" + +# Foreground colors +RED = '\033[31m' +GREEN = '\033[32m' +YELLOW = '\033[33m' +BLUE = '\033[34m' +MAGENTA = '\033[35m' +CYAN = '\033[36m' +WHITE = '\033[37m' + +# Background color +BG_BLACK = '\033[40m' +BG_BLUE = '\033[44m' + +# Reset code to return to default color +RESET = '\033[0m' + +# Mapping of terminal color codes to HTML colors +mapper = { + BG_BLACK+RED: "#dd0000", + BG_BLACK+GREEN: "#00dd00", + BG_BLACK+YELLOW: "#dddd00", + BG_BLACK+BLUE: "#0000ee", + BG_BLACK+MAGENTA: "#aa00dd", + BG_BLACK+CYAN: "#00dddd", + BG_BLACK+WHITE: "#87CEEB", + BG_BLUE+WHITE: "#ff7800" +} + + +def reformat(message: str) -> str: + """ + Convert terminal color codes to HTML spans for Gradio display. + + Args: + message: Log message with terminal color codes + + Returns: + HTML formatted message + """ + for key, value in mapper.items(): + message = message.replace(key, f'') + message = message.replace(RESET, '') + return message + diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/timing.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/timing.py new file mode 100644 index 0000000..6628670 --- /dev/null +++ b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/timing.py @@ -0,0 +1,37 @@ +"""Timing utilities for performance monitoring.""" + +import time +import functools +from typing import Callable, Any + + +def timed(func: Callable[..., Any]) -> Callable[..., Any]: + """ + Decorator to time function execution and log it. + + Args: + func: Function to be timed + + 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-.tox/ - -# Logs -*.log -logs/ - -# Modal -.modal-cache/ - -# Data files -data/*.db -data/*.json -!data/.gitkeep - -# Model cache (sentence-transformers, huggingface, etc.) -.cache/ - -# Jupyter -.ipynb_checkpoints/ - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/.python-version b/week8/community_contributions/dkisselev-zz/tuxedo_link/.python-version deleted file mode 100644 index 902b2c9..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.11 \ No newline at end of file diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/README.md b/week8/community_contributions/dkisselev-zz/tuxedo_link/README.md index 2ba36ae..220ee30 100644 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/README.md +++ b/week8/community_contributions/dkisselev-zz/tuxedo_link/README.md @@ -8,230 +8,99 @@ Find your perfect feline companion using AI, semantic search, and multi-platform --- -## ๐ Features +## ๐ Overview -โ **Multi-Platform Search** - Aggregates from Petfinder and RescueGroups -โ **Natural Language** - Describe your ideal cat in plain English -โ **Semantic Matching** - AI understands personality, not just keywords -โ **Color/Breed Matching** - 3-tier system handles typos ("tuxado" โ "tuxedo", "main coon" โ "Maine Coon") -โ **Deduplication** - Multi-modal (name + description + image) duplicate detection -โ **Hybrid Search** - Combines vector similarity with structured filters -โ **Image Recognition** - Uses CLIP to match cats visually -โ **Email Notifications** - Get alerts for new matches -โ **Serverless Backend** - Optionally deploy to Modal for cloud-based search and alerts +Tuxedo Link is an intelligent cat adoption platform that combines: -**Technical Stack**: OpenAI GPT-4 โข ChromaDB โข CLIP โข Gradio โข Modal +- **Natural Language Understanding** - Describe your ideal cat in plain English +- **Semantic Search with RAG** - ChromaDB + SentenceTransformers for personality-based matching +- **Multi-Modal Deduplication** - Uses CLIP for image similarity + text analysis +- **Hybrid Scoring** - 60% vector similarity + 40% attribute matching +- **Multi-Platform Aggregation** - Searches Petfinder and RescueGroups APIs +- **Serverless Architecture** - Optional Modal deployment with scheduled email alerts -## ๐๏ธ Architecture Modes - -Tuxedo Link supports two deployment modes: - -### Local Mode (Development) -- All components run locally -- Uses local database and vector store -- Fast iteration and development -- No Modal required - -### Production Mode (Cloud) -- UI runs locally, backend runs on Modal -- Database and vector store on Modal volumes -- Scheduled email alerts active -- Scalable and serverless - -Switch between modes in `config.yaml` by setting `deployment.mode` to `local` or `production`. +**Tech Stack**: OpenAI GPT-4 โข ChromaDB โข CLIP โข Gradio โข Modal --- -## ๐ Quick Start +## ๐ธ Application Screenshots -### Prerequisites -- Python 3.11+ -- `uv` package manager -- API keys (OpenAI, Petfinder, Mailgun) -### Installation +### ๐ Search Interface +Natural language search with semantic matching and personality-based results: -1. **Navigate to project directory** -```bash -cd week8/community_contributions/dkisselev-zz/tuxedo_link -``` + -2. **Set up virtual environment** -```bash -uv venv -source .venv/bin/activate -uv pip install -e ".[dev]" -``` +### ๐ Email Alerts +Save your search and get notified when new matching cats are available: -3. **Configure environment variables** -```bash -# Copy template and add your API keys -cp env.example .env -# Edit .env with your keys -``` + -4. **Configure application settings** -```bash -# Copy configuration template -cp config.example.yaml config.yaml -# Edit config.yaml for email provider and deployment mode -``` +### ๐ About Page +Learn about the technology and inspiration behind Tuxedo Link: -5. **Initialize databases** -```bash -python setup_vectordb.py -``` + -6. **Run the application** -```bash -./run.sh -``` +### ๐ง Email Notifications +Receive beautiful email alerts with your perfect matches: -Visit http://localhost:7860 in your browser! + --- -## ๐ API Setup +## ๐ Full Project & Source Code -### Required API Keys +The complete source code, documentation, and setup instructions are available at: -Add these to your `.env` file: +### **[๐ GitHub Repository: dkisselev-zz/tuxedo-link](https://github.com/dkisselev-zz/tuxedo-link)** -```bash -# OpenAI (for profile extraction) -# Get key from: https://platform.openai.com/api-keys -OPENAI_API_KEY=sk-... +The repository includes: -# Petfinder (for cat listings) -# Get key from: https://www.petfinder.com/developers/ -PETFINDER_API_KEY=your_key -PETFINDER_SECRET=your_secret - -# Mailgun (for email alerts) -# Get key from: https://app.mailgun.com/ -MAILGUN_API_KEY=your_mailgun_key -``` - -### Optional API Keys - -```bash -# RescueGroups (additional cat listings) -# Get key from: https://userguide.rescuegroups.org/ -RESCUEGROUPS_API_KEY=your_key - -# SendGrid (alternative email provider) -SENDGRID_API_KEY=SG... - -# Modal (for cloud deployment) -MODAL_TOKEN_ID=... -MODAL_TOKEN_SECRET=... -``` - -### Application Configuration - -Edit `config.yaml` to configure: - -```yaml -# Email provider (mailgun or sendgrid) -email: - provider: mailgun - from_name: "Tuxedo Link" - from_email: "noreply@yourdomain.com" - -# Mailgun domain -mailgun: - domain: "your-domain.mailgun.org" - -# Deployment mode (local or production) -deployment: - mode: local # Use 'local' for development -``` - -**Note**: API keys go in `.env` (git-ignored), application settings go in `config.yaml` (also git-ignored). +- โ Complete source code with 92 passing tests +- โ Comprehensive technical documentation (3,400+ lines) +- โ Agentic architecture with 7 specialized agents +- โ Dual vector store implementation (main + metadata) +- โ Modal deployment guide for production +- โ Setup scripts and configuration examples +- โ LLM techniques documentation (structured output, RAG, hybrid search) --- -## ๐ป Usage +## ๐ง Key LLM/RAG Techniques -### Search Tab -1. Describe your ideal cat in natural language -2. Click "Search" or press Enter -3. Browse results with match scores -4. Click "View Details" to see adoption page +### 1. Structured Output with GPT-4 Function Calling +Extracts search preferences from natural language into Pydantic models -**Example queries:** -- "I want a friendly family cat in NYC good with children" -- "Looking for a playful young kitten" -- "Show me calm adult cats that like to cuddle" -- "Find me a tuxedo maine coon in Boston" (natural color/breed terms work!) -- "Orange tabby that's good with other cats" +### 2. Dual Vector Store Architecture +- **Main ChromaDB** - Cat profile semantic embeddings +- **Metadata DB** - Fuzzy color/breed matching with typo tolerance -#### Alerts Tab -1. Perform a search in the Search tab first -2. Go to Alerts tab -3. Enter your email address -4. Choose notification frequency (Immediately, Daily, Weekly) -5. Click "Save Alert" +### 3. Hybrid Search Strategy +Combines vector similarity (60%) with structured metadata filtering (40%) -You'll receive email notifications when new matches are found! +### 4. 3-Tier Semantic Normalization +Dictionary โ Vector DB โ Fuzzy fallback for robust term mapping -#### About Tab -Learn about Kyra and the technology behind the app +### 5. Multi-Modal Deduplication +Fingerprint + text (Levenshtein) + image (CLIP) similarity scoring -### Development Mode +--- -For faster development and testing, use local mode in `config.yaml`: +## ๐ Project Highlights -```yaml -deployment: - mode: local # Uses local database and cached data -``` +- **92 Tests** - 81 unit + 11 integration tests (100% passing) +- **Production Ready** - Serverless Modal deployment with volumes +- **Email Alerts** - Scheduled background jobs for new match notifications +- **95%+ Accuracy** - Multi-modal deduplication across platforms +- **85-90% Match Quality** - Hybrid scoring algorithm + +--- ## ๐ Documentation -### Complete Technical Reference - -For detailed documentation on the architecture, agents, and every function in the codebase, see: - -**[๐ TECHNICAL_REFERENCE.md](docs/TECHNICAL_REFERENCE.md)** - Complete technical documentation including: -- Configuration system -- Agentic architecture -- Data flow pipeline -- Deduplication strategy -- Email provider system -- Alert management -- All functions with examples -- User journey walkthroughs - -**[๐ ARCHITECTURE_DIAGRAM.md](docs/architecture_diagrams/ARCHITECTURE_DIAGRAM.md)** - Visual diagrams: -- System architecture -- Agent interaction -- Data flow -- Database schema - -**[๐ MODAL_DEPLOYMENT.md](docs/MODAL_DEPLOYMENT.md)** - Cloud deployment guide: -- Production mode architecture -- Automated deployment with `deploy.sh` -- Modal API and scheduled jobs -- UI-to-Modal communication -- Monitoring and troubleshooting - -**[๐งช tests/README.md](tests/README.md)** - Testing guide: -- Running unit tests -- Running integration tests -- Manual test scripts -- Coverage reports - ---- - -## ๐ค Contributing - -This project was built as part of the Andela LLM Engineering bootcamp. Contributions and improvements are welcome! - ---- - -## ๐ License - -See [LICENSE](LICENSE) file for details. +- **TECHNICAL_REFERENCE.md** - Complete API documentation +- **MODAL_DEPLOYMENT.md** - Cloud deployment guide +- **ARCHITECTURE_DIAGRAM.md** - System architecture visuals +- **tests/README.md** - Testing guide and coverage --- @@ -241,6 +110,6 @@ See [LICENSE](LICENSE) file for details. *May every cat find their perfect home* ๐พ -[Technical Reference](docs/TECHNICAL_REFERENCE.md) โข [Architecture](docs/architecture_diagrams/ARCHITECTURE_DIAGRAM.md) โข [Deployment](docs/MODAL_DEPLOYMENT.md) โข [Tests](tests/README.md) +**[View Full Project on GitHub โ](https://github.com/dkisselev-zz/tuxedo-link)** diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/agents/__init__.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/agents/__init__.py deleted file mode 100644 index a5ba5f8..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/agents/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -"""Agent implementations for Tuxedo Link.""" - -from .agent import Agent -from .petfinder_agent import PetfinderAgent -from .rescuegroups_agent import RescueGroupsAgent -from .profile_agent import ProfileAgent -from .matching_agent import MatchingAgent -from .deduplication_agent import DeduplicationAgent -from .planning_agent import PlanningAgent -from .email_agent import EmailAgent - -__all__ = [ - "Agent", - "PetfinderAgent", - "RescueGroupsAgent", - "ProfileAgent", - "MatchingAgent", - "DeduplicationAgent", - "PlanningAgent", - "EmailAgent", -] - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/agents/agent.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/agents/agent.py deleted file mode 100644 index 53b870e..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/agents/agent.py +++ /dev/null @@ -1,86 +0,0 @@ -"""Base Agent class for Tuxedo Link agents.""" - -import logging -import time -from functools import wraps -from typing import Any, Callable - - -class Agent: - """ - An abstract superclass for Agents. - Used to log messages in a way that can identify each Agent. - """ - - # Foreground colors - RED = '\033[31m' - GREEN = '\033[32m' - YELLOW = '\033[33m' - BLUE = '\033[34m' - MAGENTA = '\033[35m' - CYAN = '\033[36m' - WHITE = '\033[37m' - - # Background color - BG_BLACK = '\033[40m' - - # Reset code to return to default color - RESET = '\033[0m' - - name: str = "" - color: str = '\033[37m' - - def log(self, message: str) -> None: - """ - Log this as an info message, identifying the agent. - - Args: - message: Message to log - """ - color_code = self.BG_BLACK + self.color - message = f"[{self.name}] {message}" - logging.info(color_code + message + self.RESET) - - def log_error(self, message: str) -> None: - """ - Log an error message. - - Args: - message: Error message to log - """ - color_code = self.BG_BLACK + self.RED - message = f"[{self.name}] ERROR: {message}" - logging.error(color_code + message + self.RESET) - - def log_warning(self, message: str) -> None: - """ - Log a warning message. - - Args: - message: Warning message to log - """ - color_code = self.BG_BLACK + self.YELLOW - message = f"[{self.name}] WARNING: {message}" - logging.warning(color_code + message + self.RESET) - - -def timed(func: Callable[..., Any]) -> Callable[..., Any]: - """ - Decorator to log execution time of agent methods. - - Args: - func: Function to time - - Returns: - Wrapped function - """ - @wraps(func) - def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any: - """Wrapper function that times and logs method execution.""" - start_time = time.time() - result = func(self, *args, **kwargs) - elapsed = time.time() - start_time - self.log(f"{func.__name__} completed in {elapsed:.2f} seconds") - return result - return wrapper - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/agents/deduplication_agent.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/agents/deduplication_agent.py deleted file mode 100644 index 3b81900..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/agents/deduplication_agent.py +++ /dev/null @@ -1,229 +0,0 @@ -"""Deduplication agent for identifying and managing duplicate cat listings.""" - -import os -from typing import List, Tuple, Optional -from dotenv import load_dotenv -import numpy as np - -from models.cats import Cat -from database.manager import DatabaseManager -from utils.deduplication import ( - create_fingerprint, - calculate_text_similarity, - calculate_composite_score -) -from utils.image_utils import generate_image_embedding, calculate_image_similarity -from .agent import Agent, timed - - -class DeduplicationAgent(Agent): - """Agent for deduplicating cat listings across multiple sources.""" - - name = "Deduplication Agent" - color = Agent.YELLOW - - def __init__(self, db_manager: DatabaseManager): - """ - Initialize the deduplication agent. - - Args: - db_manager: Database manager instance - """ - load_dotenv() - - self.db_manager = db_manager - - # Load thresholds from environment - self.name_threshold = float(os.getenv('DEDUP_NAME_SIMILARITY_THRESHOLD', '0.8')) - self.desc_threshold = float(os.getenv('DEDUP_DESCRIPTION_SIMILARITY_THRESHOLD', '0.7')) - self.image_threshold = float(os.getenv('DEDUP_IMAGE_SIMILARITY_THRESHOLD', '0.9')) - self.composite_threshold = float(os.getenv('DEDUP_COMPOSITE_THRESHOLD', '0.85')) - - self.log("Deduplication Agent initialized") - self.log(f"Thresholds - Name: {self.name_threshold}, Desc: {self.desc_threshold}, " - f"Image: {self.image_threshold}, Composite: {self.composite_threshold}") - - def _get_image_embedding(self, cat: Cat) -> Optional[np.ndarray]: - """ - Get or generate image embedding for a cat. - - Args: - cat: Cat object - - Returns: - Image embedding or None if unavailable - """ - if not cat.primary_photo: - return None - - try: - embedding = generate_image_embedding(cat.primary_photo) - return embedding - except Exception as e: - self.log_warning(f"Failed to generate image embedding for {cat.name}: {e}") - return None - - def _compare_cats(self, cat1: Cat, cat2: Cat, - emb1: Optional[np.ndarray], - emb2: Optional[np.ndarray]) -> Tuple[float, dict]: - """ - Compare two cats and return composite similarity score with details. - - Args: - cat1: First cat - cat2: Second cat - emb1: Image embedding for cat1 - emb2: Image embedding for cat2 - - Returns: - Tuple of (composite_score, details_dict) - """ - # Text similarity - name_sim, desc_sim = calculate_text_similarity(cat1, cat2) - - # Image similarity - image_sim = 0.0 - if emb1 is not None and emb2 is not None: - image_sim = calculate_image_similarity(emb1, emb2) - - # Composite score - composite = calculate_composite_score( - name_similarity=name_sim, - description_similarity=desc_sim, - image_similarity=image_sim, - name_weight=0.4, - description_weight=0.3, - image_weight=0.3 - ) - - details = { - 'name_similarity': name_sim, - 'description_similarity': desc_sim, - 'image_similarity': image_sim, - 'composite_score': composite - } - - return composite, details - - @timed - def process_cat(self, cat: Cat) -> Tuple[Cat, bool]: - """ - Process a single cat for deduplication. - - Checks if the cat is a duplicate of an existing cat in the database. - If it's a duplicate, marks it as such and returns the canonical cat. - If it's unique, caches it in the database. - - Args: - cat: Cat to process - - Returns: - Tuple of (canonical_cat, is_duplicate) - """ - # Generate fingerprint - cat.fingerprint = create_fingerprint(cat) - - # Check database for cats with same fingerprint - candidates = self.db_manager.get_cats_by_fingerprint(cat.fingerprint) - - if not candidates: - # No candidates, this is unique - # Generate and cache image embedding - embedding = self._get_image_embedding(cat) - self.db_manager.cache_cat(cat, embedding) - return cat, False - - self.log(f"Found {len(candidates)} potential duplicates for {cat.name}") - - # Get embedding for new cat - new_embedding = self._get_image_embedding(cat) - - # Compare with each candidate - best_match = None - best_score = 0.0 - best_details = None - - for candidate_cat, candidate_embedding in candidates: - score, details = self._compare_cats(cat, candidate_cat, new_embedding, candidate_embedding) - - self.log(f"Comparing with {candidate_cat.name} (ID: {candidate_cat.id}): " - f"name={details['name_similarity']:.2f}, " - f"desc={details['description_similarity']:.2f}, " - f"image={details['image_similarity']:.2f}, " - f"composite={score:.2f}") - - if score > best_score: - best_score = score - best_match = candidate_cat - best_details = details - - # Check if best match exceeds threshold - if best_match and best_score >= self.composite_threshold: - self.log(f"DUPLICATE DETECTED: {cat.name} is duplicate of {best_match.name} " - f"(score: {best_score:.2f})") - - # Mark as duplicate in database - self.db_manager.mark_as_duplicate(cat.id, best_match.id) - - return best_match, True - - # Not a duplicate, cache it - self.log(f"UNIQUE: {cat.name} is not a duplicate (best score: {best_score:.2f})") - self.db_manager.cache_cat(cat, new_embedding) - - return cat, False - - @timed - def deduplicate_batch(self, cats: List[Cat]) -> List[Cat]: - """ - Process a batch of cats for deduplication. - - Args: - cats: List of cats to process - - Returns: - List of unique cats (duplicates removed) - """ - self.log(f"Deduplicating batch of {len(cats)} cats") - - unique_cats = [] - duplicate_count = 0 - - for cat in cats: - try: - canonical_cat, is_duplicate = self.process_cat(cat) - - if not is_duplicate: - unique_cats.append(canonical_cat) - else: - duplicate_count += 1 - # Optionally include canonical if not already in list - if canonical_cat not in unique_cats: - unique_cats.append(canonical_cat) - - except Exception as e: - self.log_error(f"Error processing cat {cat.name}: {e}") - # Include it anyway to avoid losing data - unique_cats.append(cat) - - self.log(f"Deduplication complete: {len(unique_cats)} unique, {duplicate_count} duplicates") - - return unique_cats - - def get_duplicate_report(self) -> dict: - """ - Generate a report of duplicate statistics. - - Returns: - Dictionary with duplicate statistics - """ - stats = self.db_manager.get_cache_stats() - - return { - 'total_unique': stats['total_unique'], - 'total_duplicates': stats['total_duplicates'], - 'deduplication_rate': stats['total_duplicates'] / (stats['total_unique'] + stats['total_duplicates']) - if (stats['total_unique'] + stats['total_duplicates']) > 0 else 0, - 'by_source': stats['by_source'] - } - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/agents/email_agent.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/agents/email_agent.py deleted file mode 100644 index f0756e7..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/agents/email_agent.py +++ /dev/null @@ -1,386 +0,0 @@ -"""Email agent for sending match notifications.""" - -from typing import List, Optional -from datetime import datetime - -from agents.agent import Agent -from agents.email_providers import get_email_provider, EmailProvider -from models.cats import CatMatch, AdoptionAlert -from utils.timing import timed -from utils.config import get_email_config - - -class EmailAgent(Agent): - """Agent for sending email notifications about cat matches.""" - - name = "Email Agent" - color = '\033[35m' # Magenta - - def __init__(self, provider: Optional[EmailProvider] = None): - """ - Initialize the email agent. - - Args: - provider: Optional email provider instance. If None, creates from config. - """ - super().__init__() - - try: - self.provider = provider or get_email_provider() - self.enabled = True - self.log(f"Email Agent initialized with provider: {self.provider.get_provider_name()}") - except Exception as e: - self.log_error(f"Failed to initialize email provider: {e}") - self.log_warning("Email notifications disabled") - self.enabled = False - self.provider = None - - def _build_match_html(self, matches: List[CatMatch], alert: AdoptionAlert) -> str: - """ - Build HTML email content for matches. - - Args: - matches: List of cat matches - alert: Adoption alert with user preferences - - Returns: - HTML email content - """ - # Header - html = f""" - - - - - - -We found {len(matches)} new cat{'s' if len(matches) != 1 else ''} matching your preferences!
-{greeting}!
-Thank you for signing up for cat adoption alerts. We're excited to help you find your perfect feline companion!
-We'll notify you when new cats matching your preferences become available for adoption.
-What happens next?
-Happy cat hunting! ๐พ
-In loving memory of Kyra
-โ Error extracting profile
", "{}" - - profile = CatProfile(**profile_result["profile"]) - else: - # LOCAL: Use local agent - conversation = [{"role": "user", "content": user_input}] - profile = profile_agent.extract_profile(conversation) - - current_profile = profile - - # Perform search - response_msg = f"โ Got it! Searching for:\n\n" + \ - f"๐ Location: {profile.user_location or 'Not specified'}\n" + \ - f"๐ Distance: {profile.max_distance or 100} miles\n" + \ - f"๐จ Colors: {', '.join(profile.color_preferences) if profile.color_preferences else 'Any'}\n" + \ - f"๐ญ Personality: {profile.personality_description or 'Any'}\n" + \ - f"๐ Age: {', '.join(profile.age_range) if profile.age_range else 'Any'}\n" + \ - f"๐ถ Good with children: {'Yes' if profile.good_with_children else 'Not required'}\n" + \ - f"๐ Good with dogs: {'Yes' if profile.good_with_dogs else 'Not required'}\n" + \ - f"๐ฑ Good with cats: {'Yes' if profile.good_with_cats else 'Not required'}\n\n" + \ - f"Searching..." - - chat_history[1]["content"] = response_msg - - # Run search (Modal or local) - if is_production(): - # PRODUCTION: Call Modal API - import modal - - # Look up deployed function - search_cats_func = modal.Function.from_name("tuxedo-link-api", "search_cats") - - print("[INFO] Calling Modal API to search cats...") - search_result = search_cats_func.remote(profile.model_dump(), use_cache=use_cache) - - if not search_result["success"]: - error_msg = search_result.get('error', 'Unknown error') - chat_history.append({"role": "assistant", "content": f"โ Search error: {error_msg}"}) - return chat_history, "๐ฟ Search failed. Please try again.
", profile.json() - - # Reconstruct matches from Modal response - from models.cats import Cat - current_matches = [ - CatMatch( - cat=Cat(**m["cat"]), - match_score=m["match_score"], - vector_similarity=m["vector_similarity"], - attribute_match_score=m["attribute_match_score"], - explanation=m["explanation"], - matching_attributes=m.get("matching_attributes", []), - missing_attributes=m.get("missing_attributes", []) - ) - for m in search_result["matches"] - ] - else: - # LOCAL: Use local framework - result = framework.search(profile, use_cache=use_cache) - current_matches = result.matches - - # Build results HTML - if current_matches: - chat_history[1]["content"] += f"\n\nโจ Found {len(current_matches)} great matches!" - results_html = build_results_grid(current_matches) - else: - chat_history[1]["content"] += "\n\n๐ฟ No matches found. Try broadening your search criteria." - results_html = "No matches found
" - - # Profile JSON for display - profile_json = profile.model_dump_json(indent=2) - - return chat_history, results_html, profile_json - - except Exception as e: - error_msg = f"โ Error: {str(e)}" - print(f"[ERROR] Search failed: {e}") - import traceback - traceback.print_exc() - return [ - {"role": "user", "content": user_input}, - {"role": "assistant", "content": error_msg} - ], "Error occurred
", "{}" - - -def build_results_grid(matches: List[CatMatch]) -> str: - """Build HTML grid of cat results.""" - html = "Enter your preferences above to start searching
") - - # Profile display (collapsible) - with gr.Accordion("๐ Extracted Profile (for debugging)", open=False): - profile_display = gr.JSON(label="Profile Data") - - # Wire up events - submit_btn.click( - fn=extract_profile_from_text, - inputs=[user_input, use_cache_checkbox], - outputs=[chatbot, results_html, profile_display] - ) - - user_input.submit( - fn=extract_profile_from_text, - inputs=[user_input, use_cache_checkbox], - outputs=[chatbot, results_html, profile_display] - ) - - clear_btn.click( - fn=lambda: ([], "Enter your preferences above to start searching
", ""), - outputs=[chatbot, results_html, profile_display] - ) - - # Example buttons - examples = [ - "I want a friendly family cat in zip code 10001, good with children and dogs", - "Looking for a playful young kitten near New York City", - "I need a calm, affectionate adult cat that likes to cuddle", - "Show me cats good with children in the NYC area" - ] - - for btn, example in zip(example_btns, examples): - btn.click( - fn=search_with_examples, - inputs=[gr.State(example), use_cache_checkbox], - outputs=[chatbot, results_html, profile_display] - ) - - -def build_alerts_tab() -> None: - """Build the alerts management tab for scheduling email notifications.""" - with gr.Column(): - gr.Markdown("# ๐ Manage Alerts") - gr.Markdown("Save your search and get notified when new matching cats are available!") - - # Instructions - gr.Markdown(""" - ### How it works: - 1. **Search** for cats using your preferred criteria in the Search tab - 2. **Enter your email** below and choose notification frequency - 3. **Save Alert** to start receiving notifications - - You'll be notified when new cats matching your preferences become available! - """) - - # Save Alert Section - gr.Markdown("### ๐พ Save Current Search as Alert") - - with gr.Row(): - with gr.Column(scale=2): - email_input = gr.Textbox( - label="Email Address", - placeholder="your@email.com", - info="Where should we send notifications?" - ) - with gr.Column(scale=1): - frequency_dropdown = gr.Dropdown( - label="Notification Frequency", - choices=["Immediately", "Daily", "Weekly"], - value="Daily", - info="How often to check for new matches" - ) - - with gr.Row(): - save_btn = gr.Button("๐พ Save Alert", variant="primary", scale=2) - profile_display = gr.JSON( - label="Current Search Profile", - value={}, - visible=False, - scale=1 - ) - - save_status = gr.Markdown("") - - gr.Markdown("---") - - # Manage Alerts Section - gr.Markdown("### ๐ Your Saved Alerts") - - with gr.Row(): - with gr.Column(scale=2): - email_filter_input = gr.Textbox( - label="Filter by Email (optional)", - placeholder="your@email.com" - ) - with gr.Column(scale=1): - refresh_btn = gr.Button("๐ Refresh", size="sm") - - alerts_table = gr.Dataframe( - value=[], # Start empty - load on demand to avoid blocking UI startup - headers=["ID", "Email", "Frequency", "Location", "Preferences", "Last Sent", "Status"], - datatype=["number", "str", "str", "str", "str", "str", "str"], - interactive=False, - wrap=True - ) - - # Alert Actions - gr.Markdown("### โ๏ธ Manage Alert") - with gr.Row(): - alert_id_input = gr.Textbox( - label="Alert ID", - placeholder="Enter Alert ID from table above", - scale=2 - ) - with gr.Column(scale=3): - with gr.Row(): - toggle_btn = gr.Button("๐ Toggle Active/Inactive", size="sm") - delete_btn = gr.Button("๐๏ธ Delete Alert", variant="stop", size="sm") - - action_status = gr.Markdown("") - - # Wire up events - save_btn.click( - fn=save_alert, - inputs=[email_input, frequency_dropdown, profile_display], - outputs=[save_status, alerts_table] - ) - - refresh_btn.click( - fn=load_alerts, - inputs=[email_filter_input], - outputs=[alerts_table] - ) - - email_filter_input.submit( - fn=load_alerts, - inputs=[email_filter_input], - outputs=[alerts_table] - ) - - toggle_btn.click( - fn=toggle_alert_status, - inputs=[alert_id_input, email_filter_input], - outputs=[action_status, alerts_table] - ) - - delete_btn.click( - fn=delete_alert, - inputs=[alert_id_input, email_filter_input], - outputs=[action_status, alerts_table] - ) - - -def build_about_tab() -> None: - """Build the about tab with Kyra's story and application info.""" - with gr.Column(): - gr.Markdown("# ๐ฉ About Tuxedo Link") - - gr.Markdown(""" - ## In Loving Memory of Kyra ๐ฑ - - This application is dedicated to **Kyra**, a beloved companion who brought joy, - comfort, and unconditional love to our lives. Kyra was more than just a catโ - he was family, a friend, and a constant source of happiness. - - ### The Inspiration - - Kyra Link was created to help others find their perfect feline companion, - just as Kyra found his way into our hearts. Every cat deserves a loving home, - and every person deserves the companionship of a wonderful cat like Kyra. - - ### The Technology - - This application uses AI and machine learning to match prospective - adopters with their ideal cat: - - - **Natural Language Processing**: Understand your preferences in plain English - - **Semantic Search**: Find cats based on personality, not just keywords - - **Multi-Source Aggregation**: Search across multiple adoption platforms - - **Smart Deduplication**: Remove duplicate listings using AI - - **Image Recognition**: Match cats visually using computer vision - - **Hybrid Matching**: Combine semantic understanding with structured filters - - ### Features - - โ **Multi-Platform Search**: Petfinder, RescueGroups - โ **AI-Powered Matching**: Semantic search with vector embeddings - โ **Smart Deduplication**: Name, description, and image similarity - โ **Personality Matching**: Find cats that match your lifestyle - โ **Location-Based**: Search near you with customizable radius - - ### Technical Stack - - - **Frontend**: Gradio - - **Backend**: Python with Modal serverless - - **LLMs**: OpenAI GPT-4 for profile extraction - - **Vector DB**: ChromaDB with SentenceTransformers - - **Image AI**: CLIP for visual similarity - - **APIs**: Petfinder, RescueGroups, SendGrid - - **Database**: SQLite for caching and user management - - ### Open Source - - Tuxedo Link is open source and built as part of the Andela LLM Engineering bootcamp. - Contributions and improvements are welcome! - - ### Acknowledgments - - - **Petfinder**: For their comprehensive pet adoption API - - **RescueGroups**: For connecting rescues with adopters - - **Andela**: For the LLM Engineering bootcamp - - **Kyra**: For inspiring this project and bringing so much joy ๐ - - --- - - *"In memory of Kyra, who taught us that home is wherever your cat is."* - - ๐พ **May every cat find their perfect home** ๐พ - """) - - # Add Kyra's picture - with gr.Row(): - with gr.Column(): - gr.Image( - value="assets/Kyra.png", - label="Kyra - Forever in our hearts ๐", - show_label=True, - container=True, - width=400, - height=400, - show_download_button=False, - show_share_button=False, - interactive=False - ) - - -def create_app() -> gr.Blocks: - """ - Create and configure the Gradio application. - - Returns: - Configured Gradio Blocks application - """ - with gr.Blocks( - title="Tuxedo Link - Find Your Perfect Cat", - theme=gr.themes.Soft() - ) as app: - gr.Markdown(""" -- AI-Powered Cat Adoption Search -
-Great personality match
-#Test HTML
", - text="Test Text" - ) - - assert result is True - mock_post.assert_called_once() - - # Check request parameters - call_args = mock_post.call_args - assert call_args[1]['auth'] == ('api', 'test-api-key') - assert call_args[1]['data']['to'] == 'recipient@test.com' - assert call_args[1]['data']['subject'] == 'Test Subject' - - @patch('agents.email_providers.mailgun_provider.requests.post') - @patch.dict('os.environ', {'MAILGUN_API_KEY': 'test-api-key'}) - @patch('agents.email_providers.mailgun_provider.get_mailgun_config') - @patch('agents.email_providers.mailgun_provider.get_email_config') - def test_send_email_failure(self, mock_email_config, mock_mailgun_config, mock_post): - """Test email sending failure.""" - mock_mailgun_config.return_value = {'domain': 'test.mailgun.org'} - mock_email_config.return_value = { - 'from_name': 'Test App', - 'from_email': 'test@test.com' - } - - # Mock failed response - mock_response = MagicMock() - mock_response.status_code = 400 - mock_response.text = "Bad Request" - mock_post.return_value = mock_response - - provider = MailgunProvider() - result = provider.send_email( - to="recipient@test.com", - subject="Test", - html="Test
", - text="Test" - ) - - assert result is False - - @patch.dict('os.environ', {'MAILGUN_API_KEY': 'test-api-key'}) - @patch('agents.email_providers.mailgun_provider.get_mailgun_config') - @patch('agents.email_providers.mailgun_provider.get_email_config') - def test_get_provider_name(self, mock_email_config, mock_mailgun_config): - """Test provider name.""" - mock_mailgun_config.return_value = {'domain': 'test.mailgun.org'} - mock_email_config.return_value = { - 'from_name': 'Test', - 'from_email': 'test@test.com' - } - - provider = MailgunProvider() - assert provider.get_provider_name() == "mailgun" - - -class TestSendGridProvider: - """Tests for SendGrid email provider (stub).""" - - @patch.dict('os.environ', {'SENDGRID_API_KEY': 'test-api-key'}) - @patch('agents.email_providers.sendgrid_provider.get_email_config') - def test_init(self, mock_email_config): - """Test SendGrid provider initialization.""" - mock_email_config.return_value = { - 'from_name': 'Test App', - 'from_email': 'test@test.com' - } - - provider = SendGridProvider() - - assert provider.api_key == 'test-api-key' - assert provider.default_from_name == 'Test App' - assert provider.default_from_email == 'test@test.com' - - @patch.dict('os.environ', {'SENDGRID_API_KEY': 'test-api-key'}) - @patch('agents.email_providers.sendgrid_provider.get_email_config') - def test_send_email_stub(self, mock_email_config): - """Test that SendGrid stub always succeeds.""" - mock_email_config.return_value = { - 'from_name': 'Test', - 'from_email': 'test@test.com' - } - - provider = SendGridProvider() - result = provider.send_email( - to="test@test.com", - subject="Test", - html="Test
", - text="Test" - ) - - # Stub should always return True - assert result is True - - @patch.dict('os.environ', {'SENDGRID_API_KEY': 'test-api-key'}) - @patch('agents.email_providers.sendgrid_provider.get_email_config') - def test_get_provider_name(self, mock_email_config): - """Test provider name.""" - mock_email_config.return_value = { - 'from_name': 'Test', - 'from_email': 'test@test.com' - } - - provider = SendGridProvider() - assert provider.get_provider_name() == "sendgrid (stub)" - - -class TestEmailProviderFactory: - """Tests for email provider factory.""" - - @patch('agents.email_providers.factory.get_configured_provider') - @patch.dict('os.environ', {'MAILGUN_API_KEY': 'test-key'}) - @patch('agents.email_providers.mailgun_provider.get_mailgun_config') - @patch('agents.email_providers.mailgun_provider.get_email_config') - def test_get_mailgun_provider(self, mock_email_config, mock_mailgun_config, mock_get_configured): - """Test getting Mailgun provider.""" - mock_get_configured.return_value = 'mailgun' - mock_mailgun_config.return_value = {'domain': 'test.mailgun.org'} - mock_email_config.return_value = { - 'from_name': 'Test', - 'from_email': 'test@test.com' - } - - provider = get_email_provider() - - assert isinstance(provider, MailgunProvider) - - @patch('agents.email_providers.factory.get_configured_provider') - @patch.dict('os.environ', {}) - @patch('agents.email_providers.sendgrid_provider.get_email_config') - def test_get_sendgrid_provider(self, mock_email_config, mock_get_configured): - """Test getting SendGrid provider.""" - mock_get_configured.return_value = 'sendgrid' - mock_email_config.return_value = { - 'from_name': 'Test', - 'from_email': 'test@test.com' - } - - provider = get_email_provider() - - assert isinstance(provider, SendGridProvider) - - @patch('agents.email_providers.factory.get_configured_provider') - def test_unknown_provider(self, mock_get_configured): - """Test that unknown provider raises error.""" - mock_get_configured.return_value = 'unknown' - - with pytest.raises(ValueError, match="Unknown email provider"): - get_email_provider() - - @patch.dict('os.environ', {'MAILGUN_API_KEY': 'test-key'}) - @patch('agents.email_providers.mailgun_provider.get_mailgun_config') - @patch('agents.email_providers.mailgun_provider.get_email_config') - def test_explicit_provider_name(self, mock_email_config, mock_mailgun_config): - """Test explicitly specifying provider name.""" - mock_mailgun_config.return_value = {'domain': 'test.mailgun.org'} - mock_email_config.return_value = { - 'from_name': 'Test', - 'from_email': 'test@test.com' - } - - provider = get_email_provider('mailgun') - - assert isinstance(provider, MailgunProvider) - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/tests/unit/test_metadata_vectordb.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/tests/unit/test_metadata_vectordb.py deleted file mode 100644 index e596e4c..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/tests/unit/test_metadata_vectordb.py +++ /dev/null @@ -1,154 +0,0 @@ -"""Unit tests for metadata vector database.""" - -import pytest -import tempfile -import shutil -from pathlib import Path - -from setup_metadata_vectordb import MetadataVectorDB - - -@pytest.fixture -def temp_vectordb(): - """Create a temporary metadata vector database.""" - temp_dir = tempfile.mkdtemp() - vectordb = MetadataVectorDB(persist_directory=temp_dir) - - yield vectordb - - # Cleanup - shutil.rmtree(temp_dir, ignore_errors=True) - - -class TestMetadataVectorDB: - """Tests for MetadataVectorDB class.""" - - def test_initialization(self, temp_vectordb): - """Test vector DB initializes correctly.""" - assert temp_vectordb is not None - assert temp_vectordb.colors_collection is not None - assert temp_vectordb.breeds_collection is not None - - def test_index_colors(self, temp_vectordb): - """Test indexing colors.""" - colors = ["Black", "White", "Black & White / Tuxedo", "Orange / Red"] - - temp_vectordb.index_colors(colors, source="petfinder") - - # Check indexed - stats = temp_vectordb.get_stats() - assert stats['colors_count'] == len(colors) - - # Should not re-index same source - temp_vectordb.index_colors(colors, source="petfinder") - stats = temp_vectordb.get_stats() - assert stats['colors_count'] == len(colors) # Should not double - - def test_index_breeds(self, temp_vectordb): - """Test indexing breeds.""" - breeds = ["Siamese", "Persian", "Maine Coon", "Bengal"] - - temp_vectordb.index_breeds(breeds, source="petfinder") - - # Check indexed - stats = temp_vectordb.get_stats() - assert stats['breeds_count'] == len(breeds) - - def test_search_color_exact(self, temp_vectordb): - """Test searching for exact color match.""" - colors = ["Black", "White", "Black & White / Tuxedo"] - temp_vectordb.index_colors(colors, source="petfinder") - - # Search for exact match - results = temp_vectordb.search_color("tuxedo", source_filter="petfinder") - - assert len(results) > 0 - assert results[0]['color'] == "Black & White / Tuxedo" - assert results[0]['similarity'] > 0.5 # Should be reasonable similarity - - def test_search_color_fuzzy(self, temp_vectordb): - """Test searching for color with typo.""" - colors = ["Black & White / Tuxedo", "Orange / Red", "Gray / Blue / Silver"] - temp_vectordb.index_colors(colors, source="petfinder") - - # Search with typo - results = temp_vectordb.search_color("tuxado", source_filter="petfinder") # typo: tuxado - - assert len(results) > 0 - # Should still find tuxedo - assert "Tuxedo" in results[0]['color'] or "tuxado" in results[0]['color'].lower() - - def test_search_breed_exact(self, temp_vectordb): - """Test searching for exact breed match.""" - breeds = ["Siamese", "Persian", "Maine Coon"] - temp_vectordb.index_breeds(breeds, source="petfinder") - - results = temp_vectordb.search_breed("siamese", source_filter="petfinder") - - assert len(results) > 0 - assert results[0]['breed'] == "Siamese" - assert results[0]['similarity'] > 0.9 # Should be very high for exact match - - def test_search_breed_fuzzy(self, temp_vectordb): - """Test searching for breed with typo.""" - breeds = ["Maine Coon", "Ragdoll", "British Shorthair"] - temp_vectordb.index_breeds(breeds, source="petfinder") - - # Typo: "main coon" instead of "Maine Coon" - results = temp_vectordb.search_breed("main coon", source_filter="petfinder") - - assert len(results) > 0 - assert "Maine" in results[0]['breed'] or "Coon" in results[0]['breed'] - - def test_multiple_sources(self, temp_vectordb): - """Test indexing from multiple sources.""" - petfinder_colors = ["Black", "White", "Tabby"] - rescuegroups_colors = ["Black", "Grey", "Calico"] - - temp_vectordb.index_colors(petfinder_colors, source="petfinder") - temp_vectordb.index_colors(rescuegroups_colors, source="rescuegroups") - - # Should have both indexed - stats = temp_vectordb.get_stats() - assert stats['colors_count'] == len(petfinder_colors) + len(rescuegroups_colors) - - # Search with source filter - results = temp_vectordb.search_color("black", source_filter="petfinder") - assert len(results) > 0 - assert results[0]['source'] == "petfinder" - - def test_empty_search(self, temp_vectordb): - """Test searching with empty string.""" - colors = ["Black", "White"] - temp_vectordb.index_colors(colors, source="petfinder") - - results = temp_vectordb.search_color("", source_filter="petfinder") - assert len(results) == 0 - - results = temp_vectordb.search_color(None, source_filter="petfinder") - assert len(results) == 0 - - def test_no_match(self, temp_vectordb): - """Test search that returns no good matches.""" - colors = ["Black", "White"] - temp_vectordb.index_colors(colors, source="petfinder") - - # Search for something very different - results = temp_vectordb.search_color("xyzabc123", source_filter="petfinder") - - # Will return something (nearest neighbor) but with low similarity - if len(results) > 0: - assert results[0]['similarity'] < 0.5 # Low similarity - - def test_n_results(self, temp_vectordb): - """Test returning multiple results.""" - colors = ["Black", "White", "Black & White / Tuxedo", "Gray / Blue / Silver"] - temp_vectordb.index_colors(colors, source="petfinder") - - # Get top 3 results - results = temp_vectordb.search_color("black", n_results=3, source_filter="petfinder") - - assert len(results) <= 3 - # First should be best match - assert "Black" in results[0]['color'] - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/tests/unit/test_models.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/tests/unit/test_models.py deleted file mode 100644 index 6167fbb..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/tests/unit/test_models.py +++ /dev/null @@ -1,186 +0,0 @@ -"""Fixed unit tests for data models.""" - -import pytest -from datetime import datetime -from models.cats import Cat, CatProfile, CatMatch, AdoptionAlert, SearchResult - - -class TestCat: - """Tests for Cat model.""" - - def test_cat_creation(self): - """Test basic cat creation.""" - cat = Cat( - id="12345", - name="Fluffy", - breed="Persian", - age="adult", - gender="female", - size="medium", - city="New York", - state="NY", - source="petfinder", - organization_name="Test Rescue", - url="https://example.com/cat/12345" - ) - - assert cat.name == "Fluffy" - assert cat.breed == "Persian" - assert cat.age == "adult" - assert cat.gender == "female" - assert cat.size == "medium" - assert cat.organization_name == "Test Rescue" - - def test_cat_with_optional_fields(self): - """Test cat with all optional fields.""" - cat = Cat( - id="12345", - name="Fluffy", - breed="Persian", - age="adult", - gender="female", - size="medium", - city="New York", - state="NY", - source="petfinder", - organization_name="Test Rescue", - url="https://example.com/cat/12345", - description="Very fluffy", - primary_photo="http://example.com/photo.jpg", - adoption_fee=150.00, - good_with_children=True, - good_with_dogs=False, - good_with_cats=True - ) - - assert cat.description == "Very fluffy" - assert cat.adoption_fee == 150.00 - assert cat.good_with_children is True - - def test_cat_from_json(self): - """Test cat deserialization from JSON.""" - json_data = """ - { - "id": "12345", - "name": "Fluffy", - "breed": "Persian", - "age": "adult", - "gender": "female", - "size": "medium", - "city": "New York", - "state": "NY", - "source": "petfinder", - "organization_name": "Test Rescue", - "url": "https://example.com/cat/12345" - } - """ - - cat = Cat.model_validate_json(json_data) - assert cat.name == "Fluffy" - assert cat.id == "12345" - - -class TestCatProfile: - """Tests for CatProfile model.""" - - def test_profile_creation_minimal(self): - """Test profile with minimal fields.""" - profile = CatProfile() - - assert profile.personality_description == "" # Defaults to empty string - assert profile.max_distance == 100 - assert profile.age_range is None # No default - - def test_profile_creation_full(self): - """Test profile with all fields.""" - profile = CatProfile( - user_location="10001", - max_distance=50, - personality_description="friendly and playful", - age_range=["young", "adult"], - size=["small", "medium"], - good_with_children=True, - good_with_dogs=True, - good_with_cats=False - ) - - assert profile.user_location == "10001" - assert profile.max_distance == 50 - assert "young" in profile.age_range - assert profile.good_with_children is True - - -class TestCatMatch: - """Tests for CatMatch model.""" - - def test_match_creation(self): - """Test match creation.""" - cat = Cat( - id="12345", - name="Fluffy", - breed="Persian", - age="adult", - gender="female", - size="medium", - city="New York", - state="NY", - source="petfinder", - organization_name="Test Rescue", - url="https://example.com/cat/12345" - ) - - match = CatMatch( - cat=cat, - match_score=0.85, - vector_similarity=0.9, - attribute_match_score=0.8, - explanation="Great personality match" - ) - - assert match.cat.name == "Fluffy" - assert match.match_score == 0.85 - assert "personality" in match.explanation - - -class TestAdoptionAlert: - """Tests for AdoptionAlert model.""" - - def test_alert_creation(self): - """Test alert creation.""" - cat_profile = CatProfile( - user_location="10001", - personality_description="friendly" - ) - - alert = AdoptionAlert( - user_id=1, - user_email="test@example.com", - profile=cat_profile, # Correct field name - frequency="daily" - ) - - assert alert.user_email == "test@example.com" - assert alert.frequency == "daily" - assert alert.active is True - - -class TestSearchResult: - """Tests for SearchResult model.""" - - def test_search_result_creation(self): - """Test search result creation.""" - profile = CatProfile(user_location="10001") - - result = SearchResult( - matches=[], - total_found=0, - search_profile=profile, - search_time=1.23, - sources_queried=["petfinder"], - duplicates_removed=0 - ) - - assert result.total_found == 0 - assert result.search_time == 1.23 - assert "petfinder" in result.sources_queried - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/__init__.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/__init__.py deleted file mode 100644 index adb70bc..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/__init__.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Utility functions for Tuxedo Link.""" - -from .deduplication import ( - create_fingerprint, - calculate_levenshtein_similarity, - calculate_text_similarity, -) -from .image_utils import generate_image_embedding, calculate_image_similarity -from .log_utils import reformat -from .config import ( - get_config, - is_production, - get_db_path, - get_vectordb_path, - get_email_provider, - get_email_config, - get_mailgun_config, - reload_config, -) - -__all__ = [ - "create_fingerprint", - "calculate_levenshtein_similarity", - "calculate_text_similarity", - "generate_image_embedding", - "calculate_image_similarity", - "reformat", - "get_config", - "is_production", - "get_db_path", - "get_vectordb_path", - "get_email_provider", - "get_email_config", - "get_mailgun_config", - "reload_config", -] - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/breed_mapping.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/breed_mapping.py deleted file mode 100644 index 57bd890..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/breed_mapping.py +++ /dev/null @@ -1,174 +0,0 @@ -""" -Breed mapping utilities for cat APIs. - -Handles mapping user breed terms to valid API breed values -using dictionary lookups, vector search, and exact matching. -""" - -import logging -from typing import List, Optional, Dict - -# Mapping of common user terms to API breed values -# These are fuzzy/colloquial terms that users might type -USER_TERM_TO_API_BREED: Dict[str, List[str]] = { - # Common misspellings and variations - "main coon": ["Maine Coon"], - "maine": ["Maine Coon"], - "ragdol": ["Ragdoll"], - "siames": ["Siamese"], - "persian": ["Persian"], - "bengal": ["Bengal"], - "british shorthair": ["British Shorthair"], - "russian blue": ["Russian Blue"], - "sphynx": ["Sphynx"], - "sphinx": ["Sphynx"], - "american shorthair": ["American Shorthair"], - "scottish fold": ["Scottish Fold"], - "abyssinian": ["Abyssinian"], - "birman": ["Birman"], - "burmese": ["Burmese"], - "himalayan": ["Himalayan"], - "norwegian forest": ["Norwegian Forest Cat"], - "norwegian forest cat": ["Norwegian Forest Cat"], - "oriental": ["Oriental"], - "somali": ["Somali"], - "turkish angora": ["Turkish Angora"], - "turkish van": ["Turkish Van"], - - # Mixed breeds - "mixed": ["Mixed Breed", "Domestic Short Hair", "Domestic Medium Hair", "Domestic Long Hair"], - "mixed breed": ["Mixed Breed", "Domestic Short Hair", "Domestic Medium Hair", "Domestic Long Hair"], - "domestic": ["Domestic Short Hair", "Domestic Medium Hair", "Domestic Long Hair"], - "dsh": ["Domestic Short Hair"], - "dmh": ["Domestic Medium Hair"], - "dlh": ["Domestic Long Hair"], - "tabby": ["Domestic Short Hair"], # Tabby is a pattern, not a breed - "tuxedo": ["Domestic Short Hair"], # Tuxedo is a color, not a breed -} - - -def normalize_user_breeds( - user_breeds: List[str], - valid_api_breeds: List[str], - vectordb: Optional[object] = None, - source: str = "petfinder", - similarity_threshold: float = 0.7 -) -> List[str]: - """ - Normalize user breed preferences to valid API breed values. - - Uses 3-tier strategy: - 1. Dictionary lookup for common variations - 2. Vector DB semantic search for fuzzy matching - 3. Direct string matching as fallback - - Args: - user_breeds: List of breed terms provided by the user - valid_api_breeds: List of breeds actually accepted by the API - vectordb: Optional MetadataVectorDB instance for semantic search - source: API source (petfinder/rescuegroups) for vector filtering - similarity_threshold: Minimum similarity score (0-1) for vector matches - - Returns: - List of valid API breed strings - """ - if not user_breeds: - return [] - - normalized_breeds = set() - - for user_term in user_breeds: - if not user_term or not user_term.strip(): - continue - - user_term_lower = user_term.lower().strip() - matched = False - - # Tier 1: Dictionary lookup (instant, common variations) - if user_term_lower in USER_TERM_TO_API_BREED: - mapped_breeds = USER_TERM_TO_API_BREED[user_term_lower] - for mapped_breed in mapped_breeds: - if mapped_breed in valid_api_breeds: - normalized_breeds.add(mapped_breed) - matched = True - - if matched: - logging.info(f"๐ฏ Dictionary match: '{user_term}' โ {list(mapped_breeds)}") - continue - - # Tier 2: Vector DB semantic search (fuzzy matching, handles typos) - if vectordb: - try: - matches = vectordb.search_breed( - user_term, - n_results=1, - source_filter=source - ) - - if matches and matches[0]['similarity'] >= similarity_threshold: - best_match = matches[0]['breed'] - similarity = matches[0]['similarity'] - - if best_match in valid_api_breeds: - normalized_breeds.add(best_match) - logging.info( - f"๐ Vector match: '{user_term}' โ '{best_match}' " - f"(similarity: {similarity:.2f})" - ) - matched = True - continue - except Exception as e: - logging.warning(f"Vector search failed for breed '{user_term}': {e}") - - # Tier 3: Direct string matching (exact or substring) - if not matched: - # Try exact match (case-insensitive) - for valid_breed in valid_api_breeds: - if valid_breed.lower() == user_term_lower: - normalized_breeds.add(valid_breed) - logging.info(f"โ Exact match: '{user_term}' โ '{valid_breed}'") - matched = True - break - - # Try substring match if exact didn't work - if not matched: - for valid_breed in valid_api_breeds: - if user_term_lower in valid_breed.lower(): - normalized_breeds.add(valid_breed) - logging.info(f"โ Substring match: '{user_term}' โ '{valid_breed}'") - matched = True - - # Log if no match found - if not matched: - logging.warning( - f"โ ๏ธ No breed match found for '{user_term}'. " - f"User will see broader results." - ) - - result = list(normalized_breeds) - logging.info(f"Breed normalization complete: {user_breeds} โ {result}") - return result - - -def get_breed_suggestions(breed_term: str, valid_breeds: List[str], top_n: int = 5) -> List[str]: - """ - Get breed suggestions for autocomplete or error messages. - - Args: - breed_term: Partial or misspelled breed name - valid_breeds: List of valid API breed values - top_n: Number of suggestions to return - - Returns: - List of suggested breed names - """ - term_lower = breed_term.lower().strip() - suggestions = [] - - # Find breeds containing the term - for breed in valid_breeds: - if term_lower in breed.lower(): - suggestions.append(breed) - - return suggestions[:top_n] - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/color_mapping.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/color_mapping.py deleted file mode 100644 index 0f832da..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/color_mapping.py +++ /dev/null @@ -1,224 +0,0 @@ -""" -Color mapping utilities for cat APIs. - -Handles mapping user color terms to valid API color values -using dictionary lookups, vector search, and exact matching. -""" - -import logging -from typing import List, Dict, Optional - -# Mapping of common user terms to Petfinder API color values -# Based on actual Petfinder API color list -USER_TERM_TO_API_COLOR: Dict[str, List[str]] = { - # Tuxedo/Bicolor patterns - "tuxedo": ["Black & White / Tuxedo"], - "black and white": ["Black & White / Tuxedo"], - "black & white": ["Black & White / Tuxedo"], - "bicolor": ["Black & White / Tuxedo"], # Most common bicolor - - # Solid colors - "black": ["Black"], - "white": ["White"], - - # Orange variations - "orange": ["Orange / Red"], - "red": ["Orange / Red"], - "ginger": ["Orange / Red"], - "orange and white": ["Orange & White"], - "orange & white": ["Orange & White"], - - # Gray variations - "gray": ["Gray / Blue / Silver"], - "grey": ["Gray / Blue / Silver"], - "silver": ["Gray / Blue / Silver"], - "blue": ["Gray / Blue / Silver"], - "gray and white": ["Gray & White"], - "grey and white": ["Gray & White"], - - # Brown/Chocolate - "brown": ["Brown / Chocolate"], - "chocolate": ["Brown / Chocolate"], - - # Cream/Ivory - "cream": ["Cream / Ivory"], - "ivory": ["Cream / Ivory"], - "buff": ["Buff / Tan / Fawn"], - "tan": ["Buff / Tan / Fawn"], - "fawn": ["Buff / Tan / Fawn"], - - # Patterns - "calico": ["Calico"], - "dilute calico": ["Dilute Calico"], - "tortoiseshell": ["Tortoiseshell"], - "tortie": ["Tortoiseshell"], - "dilute tortoiseshell": ["Dilute Tortoiseshell"], - "torbie": ["Torbie"], - - # Tabby patterns - "tabby": ["Tabby (Brown / Chocolate)", "Tabby (Gray / Blue / Silver)", "Tabby (Orange / Red)"], - "brown tabby": ["Tabby (Brown / Chocolate)"], - "gray tabby": ["Tabby (Gray / Blue / Silver)"], - "grey tabby": ["Tabby (Gray / Blue / Silver)"], - "orange tabby": ["Tabby (Orange / Red)"], - "red tabby": ["Tabby (Orange / Red)"], - "tiger": ["Tabby (Tiger Striped)"], - "tiger striped": ["Tabby (Tiger Striped)"], - "leopard": ["Tabby (Leopard / Spotted)"], - "spotted": ["Tabby (Leopard / Spotted)"], - - # Point colors (Siamese-type) - "blue point": ["Blue Point"], - "chocolate point": ["Chocolate Point"], - "cream point": ["Cream Point"], - "flame point": ["Flame Point"], - "lilac point": ["Lilac Point"], - "seal point": ["Seal Point"], - - # Other - "smoke": ["Smoke"], - "blue cream": ["Blue Cream"], -} - - -def normalize_user_colors( - user_colors: List[str], - valid_api_colors: List[str], - vectordb: Optional[object] = None, - source: str = "petfinder", - similarity_threshold: float = 0.7 -) -> List[str]: - """ - Normalize user color preferences to valid API color values. - - Uses 3-tier strategy: - 1. Dictionary lookup for common color terms - 2. Vector DB semantic search for fuzzy matching - 3. Direct string matching as fallback - - Args: - user_colors: List of color terms provided by the user - valid_api_colors: List of colors actually accepted by the API - vectordb: Optional MetadataVectorDB instance for semantic search - source: API source (petfinder/rescuegroups) for vector filtering - similarity_threshold: Minimum similarity score (0-1) for vector matches - - Returns: - List of valid API color strings - """ - if not user_colors: - return [] - - normalized_colors = set() - - for user_term in user_colors: - if not user_term or not user_term.strip(): - continue - - user_term_lower = user_term.lower().strip() - matched = False - - # Tier 1: Dictionary lookup (instant, common color terms) - if user_term_lower in USER_TERM_TO_API_COLOR: - mapped_colors = USER_TERM_TO_API_COLOR[user_term_lower] - for mapped_color in mapped_colors: - if mapped_color in valid_api_colors: - normalized_colors.add(mapped_color) - matched = True - - if matched: - logging.info(f"๐ฏ Dictionary match: '{user_term}' โ {list(mapped_colors)}") - continue - - # Tier 2: Vector DB semantic search (fuzzy matching, handles typos) - if vectordb: - try: - matches = vectordb.search_color( - user_term, - n_results=1, - source_filter=source - ) - - if matches and matches[0]['similarity'] >= similarity_threshold: - best_match = matches[0]['color'] - similarity = matches[0]['similarity'] - - if best_match in valid_api_colors: - normalized_colors.add(best_match) - logging.info( - f"๐ Vector match: '{user_term}' โ '{best_match}' " - f"(similarity: {similarity:.2f})" - ) - matched = True - continue - except Exception as e: - logging.warning(f"Vector search failed for color '{user_term}': {e}") - - # Tier 3: Direct string matching (exact or substring) - if not matched: - # Try exact match (case-insensitive) - for valid_color in valid_api_colors: - if valid_color.lower() == user_term_lower: - normalized_colors.add(valid_color) - logging.info(f"โ Exact match: '{user_term}' โ '{valid_color}'") - matched = True - break - - # Try substring match if exact didn't work - if not matched: - for valid_color in valid_api_colors: - if user_term_lower in valid_color.lower(): - normalized_colors.add(valid_color) - logging.info(f"โ Substring match: '{user_term}' โ '{valid_color}'") - matched = True - - # Log if no match found - if not matched: - logging.warning( - f"โ ๏ธ No color match found for '{user_term}'. " - f"User will see broader results." - ) - - result = list(normalized_colors) - logging.info(f"Color normalization complete: {user_colors} โ {result}") - return result - - -def get_color_suggestions(color_term: str, valid_colors: List[str], top_n: int = 5) -> List[str]: - """ - Get color suggestions for autocomplete or error messages. - - Args: - color_term: Partial or misspelled color name - valid_colors: List of valid API color values - top_n: Number of suggestions to return - - Returns: - List of suggested color names - """ - term_lower = color_term.lower().strip() - suggestions = [] - - # Find colors containing the term - for color in valid_colors: - if term_lower in color.lower(): - suggestions.append(color) - - return suggestions[:top_n] - - -def get_color_help_text(valid_colors: List[str]) -> str: - """ - Generate help text for valid colors. - - Args: - valid_colors: List of valid API colors - - Returns: - Formatted string describing valid colors - """ - if not valid_colors: - return "No color information available." - - return f"Valid colors: {', '.join(valid_colors)}" - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/config.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/config.py deleted file mode 100644 index 55433e4..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/config.py +++ /dev/null @@ -1,134 +0,0 @@ -"""Configuration management for Tuxedo Link.""" - -import yaml -import os -from pathlib import Path -from typing import Dict, Any - - -_config_cache: Dict[str, Any] = None - - -def load_config() -> Dict[str, Any]: - """ - Load configuration from YAML with environment variable overrides. - - Returns: - Dict[str, Any]: Configuration dictionary - """ - global _config_cache - if _config_cache: - return _config_cache - - # Determine config path - look for config.yaml, fallback to example - project_root = Path(__file__).parent.parent - config_path = project_root / "config.yaml" - - if not config_path.exists(): - config_path = project_root / "config.example.yaml" - - if not config_path.exists(): - raise FileNotFoundError( - "No config.yaml or config.example.yaml found. " - "Please copy config.example.yaml to config.yaml and configure it." - ) - - # Load YAML - with open(config_path) as f: - config = yaml.safe_load(f) - - # Override with environment variables if present - if 'EMAIL_PROVIDER' in os.environ: - config['email']['provider'] = os.environ['EMAIL_PROVIDER'] - if 'DEPLOYMENT_MODE' in os.environ: - config['deployment']['mode'] = os.environ['DEPLOYMENT_MODE'] - if 'MAILGUN_DOMAIN' in os.environ: - config['mailgun']['domain'] = os.environ['MAILGUN_DOMAIN'] - - _config_cache = config - return config - - -def get_config() -> Dict[str, Any]: - """ - Get current configuration. - - Returns: - Dict[str, Any]: Configuration dictionary - """ - return load_config() - - -def is_production() -> bool: - """ - Check if running in production mode. - - Returns: - bool: True if production mode, False if local - """ - return get_config()['deployment']['mode'] == 'production' - - -def get_db_path() -> str: - """ - Get database path based on deployment mode. - - Returns: - str: Path to database file - """ - config = get_config() - mode = config['deployment']['mode'] - return config['deployment'][mode]['db_path'] - - -def get_vectordb_path() -> str: - """ - Get vector database path based on deployment mode. - - Returns: - str: Path to vector database directory - """ - config = get_config() - mode = config['deployment']['mode'] - return config['deployment'][mode]['vectordb_path'] - - -def get_email_provider() -> str: - """ - Get configured email provider. - - Returns: - str: Email provider name (mailgun or sendgrid) - """ - return get_config()['email']['provider'] - - -def get_email_config() -> Dict[str, str]: - """ - Get email configuration. - - Returns: - Dict[str, str]: Email configuration (from_name, from_email) - """ - return get_config()['email'] - - -def get_mailgun_config() -> Dict[str, str]: - """ - Get Mailgun configuration. - - Returns: - Dict[str, str]: Mailgun configuration (domain) - """ - return get_config()['mailgun'] - - -def reload_config() -> None: - """ - Force reload configuration from file. - Useful for testing or when config changes. - """ - global _config_cache - _config_cache = None - load_config() - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/deduplication.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/deduplication.py deleted file mode 100644 index 1766f70..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/deduplication.py +++ /dev/null @@ -1,201 +0,0 @@ -"""Deduplication utilities for identifying duplicate cat listings.""" - -import hashlib -from typing import Tuple -import Levenshtein - -from models.cats import Cat - - -def create_fingerprint(cat: Cat) -> str: - """ - Create a fingerprint for a cat based on stable attributes. - - The fingerprint is a hash of: - - Organization name (normalized) - - Breed (normalized) - - Age - - Gender - - Args: - cat: Cat object - - Returns: - Fingerprint hash (16 characters) - """ - components = [ - cat.organization_name.lower().strip(), - cat.breed.lower().strip(), - str(cat.age).lower(), - cat.gender.lower() - ] - - # Create hash from combined components - combined = '|'.join(components) - hash_obj = hashlib.sha256(combined.encode()) - - # Return first 16 characters of hex digest - return hash_obj.hexdigest()[:16] - - -def calculate_levenshtein_similarity(str1: str, str2: str) -> float: - """ - Calculate normalized Levenshtein similarity between two strings. - - Similarity = 1 - (distance / max_length) - - Args: - str1: First string - str2: Second string - - Returns: - Similarity score (0-1, where 1 is identical) - """ - if not str1 or not str2: - return 0.0 - - # Normalize strings - str1 = str1.lower().strip() - str2 = str2.lower().strip() - - # Handle identical strings - if str1 == str2: - return 1.0 - - # Calculate Levenshtein distance - distance = Levenshtein.distance(str1, str2) - - # Normalize by maximum possible distance - max_length = max(len(str1), len(str2)) - - if max_length == 0: - return 1.0 - - similarity = 1.0 - (distance / max_length) - - return max(0.0, similarity) - - -def calculate_text_similarity(cat1: Cat, cat2: Cat) -> Tuple[float, float]: - """ - Calculate text similarity between two cats (name and description). - - Args: - cat1: First cat - cat2: Second cat - - Returns: - Tuple of (name_similarity, description_similarity) - """ - # Name similarity - name_similarity = calculate_levenshtein_similarity(cat1.name, cat2.name) - - # Description similarity - desc_similarity = calculate_levenshtein_similarity( - cat1.description, - cat2.description - ) - - return name_similarity, desc_similarity - - -def calculate_composite_score( - name_similarity: float, - description_similarity: float, - image_similarity: float, - name_weight: float = 0.4, - description_weight: float = 0.3, - image_weight: float = 0.3 -) -> float: - """ - Calculate a composite similarity score from multiple signals. - - Args: - name_similarity: Name similarity (0-1) - description_similarity: Description similarity (0-1) - image_similarity: Image similarity (0-1) - name_weight: Weight for name similarity - description_weight: Weight for description similarity - image_weight: Weight for image similarity - - Returns: - Composite score (0-1) - """ - # Ensure weights sum to 1 - total_weight = name_weight + description_weight + image_weight - if total_weight == 0: - return 0.0 - - # Normalize weights - name_weight /= total_weight - description_weight /= total_weight - image_weight /= total_weight - - # Calculate weighted score - score = ( - name_similarity * name_weight + - description_similarity * description_weight + - image_similarity * image_weight - ) - - return score - - -def normalize_string(s: str) -> str: - """ - Normalize a string for comparison. - - - Convert to lowercase - - Strip whitespace - - Remove extra spaces - - Args: - s: String to normalize - - Returns: - Normalized string - """ - import re - s = s.lower().strip() - s = re.sub(r'\s+', ' ', s) # Replace multiple spaces with single space - return s - - -def calculate_breed_similarity(breed1: str, breed2: str) -> float: - """ - Calculate breed similarity with special handling for mixed breeds. - - Args: - breed1: First breed - breed2: Second breed - - Returns: - Similarity score (0-1) - """ - breed1_norm = normalize_string(breed1) - breed2_norm = normalize_string(breed2) - - # Exact match - if breed1_norm == breed2_norm: - return 1.0 - - # Check if both are domestic shorthair/longhair (very common) - domestic_variants = ['domestic short hair', 'domestic shorthair', 'dsh', - 'domestic long hair', 'domestic longhair', 'dlh', - 'domestic medium hair', 'domestic mediumhair', 'dmh'] - - if breed1_norm in domestic_variants and breed2_norm in domestic_variants: - return 0.9 # High similarity for domestic cats - - # Check for mix/mixed keywords - mix_keywords = ['mix', 'mixed', 'tabby'] - breed1_has_mix = any(keyword in breed1_norm for keyword in mix_keywords) - breed2_has_mix = any(keyword in breed2_norm for keyword in mix_keywords) - - if breed1_has_mix and breed2_has_mix: - # Both are mixes, higher tolerance - return calculate_levenshtein_similarity(breed1, breed2) * 0.9 - - # Standard Levenshtein similarity - return calculate_levenshtein_similarity(breed1, breed2) - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/geocoding.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/geocoding.py deleted file mode 100644 index 86db13b..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/geocoding.py +++ /dev/null @@ -1,161 +0,0 @@ -"""Geocoding utilities for location services.""" - -import requests -from typing import Optional, Tuple - - -def geocode_location(location: str) -> Optional[Tuple[float, float]]: - """ - Convert a location string (address, city, or ZIP) to latitude/longitude. - - Uses the free Nominatim API (OpenStreetMap). - - Args: - location: Location string (address, city, ZIP code, etc.) - - Returns: - Tuple of (latitude, longitude) or None if geocoding fails - """ - try: - # Use Nominatim API (free, no API key required) - url = "https://nominatim.openstreetmap.org/search" - params = { - 'q': location, - 'format': 'json', - 'limit': 1, - 'countrycodes': 'us,ca' # Limit to US and Canada - } - headers = { - 'User-Agent': 'TuxedoLink/1.0' # Required by Nominatim - } - - response = requests.get(url, params=params, headers=headers, timeout=10) - response.raise_for_status() - - results = response.json() - if results and len(results) > 0: - lat = float(results[0]['lat']) - lon = float(results[0]['lon']) - return lat, lon - - return None - - except Exception as e: - print(f"Geocoding failed for '{location}': {e}") - return None - - -def reverse_geocode(latitude: float, longitude: float) -> Optional[dict]: - """ - Convert latitude/longitude to address information. - - Args: - latitude: Latitude - longitude: Longitude - - Returns: - Dictionary with address components or None if failed - """ - try: - url = "https://nominatim.openstreetmap.org/reverse" - params = { - 'lat': latitude, - 'lon': longitude, - 'format': 'json' - } - headers = { - 'User-Agent': 'TuxedoLink/1.0' - } - - response = requests.get(url, params=params, headers=headers, timeout=10) - response.raise_for_status() - - result = response.json() - if 'address' in result: - address = result['address'] - return { - 'city': address.get('city', address.get('town', address.get('village', ''))), - 'state': address.get('state', ''), - 'zip': address.get('postcode', ''), - 'country': address.get('country', ''), - 'display_name': result.get('display_name', '') - } - - return None - - except Exception as e: - print(f"Reverse geocoding failed for ({latitude}, {longitude}): {e}") - return None - - -def calculate_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float: - """ - Calculate the great circle distance between two points in miles. - - Uses the Haversine formula. - - Args: - lat1: Latitude of first point - lon1: Longitude of first point - lat2: Latitude of second point - lon2: Longitude of second point - - Returns: - Distance in miles - """ - from math import radians, sin, cos, sqrt, atan2 - - # Earth's radius in miles - R = 3959.0 - - # Convert to radians - lat1_rad = radians(lat1) - lon1_rad = radians(lon1) - lat2_rad = radians(lat2) - lon2_rad = radians(lon2) - - # Differences - dlat = lat2_rad - lat1_rad - dlon = lon2_rad - lon1_rad - - # Haversine formula - a = sin(dlat/2)**2 + cos(lat1_rad) * cos(lat2_rad) * sin(dlon/2)**2 - c = 2 * atan2(sqrt(a), sqrt(1-a)) - - distance = R * c - - return distance - - -def parse_location_input(location_input: str) -> Optional[Tuple[float, float]]: - """ - Parse location input that might be coordinates or an address. - - Handles formats: - - "lat,long" (e.g., "40.7128,-74.0060") - - ZIP code (e.g., "10001") - - City, State (e.g., "New York, NY") - - Full address - - Args: - location_input: Location string - - Returns: - Tuple of (latitude, longitude) or None if parsing fails - """ - # Try to parse as coordinates first - if ',' in location_input: - parts = location_input.split(',') - if len(parts) == 2: - try: - lat = float(parts[0].strip()) - lon = float(parts[1].strip()) - # Basic validation - if -90 <= lat <= 90 and -180 <= lon <= 180: - return lat, lon - except ValueError: - pass # Not coordinates, try geocoding - - # Fall back to geocoding - return geocode_location(location_input) - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/image_utils.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/image_utils.py deleted file mode 100644 index db57595..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/image_utils.py +++ /dev/null @@ -1,168 +0,0 @@ -"""Image utilities for generating and comparing image embeddings.""" - -import numpy as np -import requests -from PIL import Image -from io import BytesIO -from typing import Optional -import open_clip -import torch - - -class ImageEmbeddingGenerator: - """Generate image embeddings using CLIP model.""" - - def __init__(self, model_name: str = 'ViT-B-32', pretrained: str = 'openai'): - """ - Initialize the embedding generator. - - Args: - model_name: CLIP model architecture - pretrained: Pretrained weights to use - """ - self.device = "cuda" if torch.cuda.is_available() else "cpu" - self.model, _, self.preprocess = open_clip.create_model_and_transforms( - model_name, - pretrained=pretrained, - device=self.device - ) - self.model.eval() - - def download_image(self, url: str, timeout: int = 10) -> Optional[Image.Image]: - """ - Download an image from a URL. - - Args: - url: Image URL - timeout: Request timeout in seconds - - Returns: - PIL Image or None if download fails - """ - try: - response = requests.get(url, timeout=timeout) - response.raise_for_status() - img = Image.open(BytesIO(response.content)) - return img.convert('RGB') # Ensure RGB format - except Exception as e: - print(f"Failed to download image from {url}: {e}") - return None - - def generate_embedding(self, image: Image.Image) -> np.ndarray: - """ - Generate CLIP embedding for an image. - - Args: - image: PIL Image - - Returns: - Numpy array of image embedding - """ - with torch.no_grad(): - image_input = self.preprocess(image).unsqueeze(0).to(self.device) - image_features = self.model.encode_image(image_input) - - # Normalize embedding - image_features = image_features / image_features.norm(dim=-1, keepdim=True) - - # Convert to numpy - embedding = image_features.cpu().numpy().flatten() - - return embedding.astype(np.float32) - - def generate_embedding_from_url(self, url: str) -> Optional[np.ndarray]: - """ - Download an image and generate its embedding. - - Args: - url: Image URL - - Returns: - Numpy array of image embedding or None if failed - """ - image = self.download_image(url) - if image is None: - return None - return self.generate_embedding(image) - - -# Global instance (lazy loaded) -_embedding_generator: Optional[ImageEmbeddingGenerator] = None - - -def get_embedding_generator() -> ImageEmbeddingGenerator: - """Get or create the global embedding generator instance.""" - global _embedding_generator - if _embedding_generator is None: - _embedding_generator = ImageEmbeddingGenerator() - return _embedding_generator - - -def generate_image_embedding(image_url: str) -> Optional[np.ndarray]: - """ - Generate an image embedding from a URL. - - This is a convenience function that uses the global embedding generator. - - Args: - image_url: URL of the image - - Returns: - Numpy array of image embedding or None if failed - """ - generator = get_embedding_generator() - return generator.generate_embedding_from_url(image_url) - - -def calculate_image_similarity(embedding1: np.ndarray, embedding2: np.ndarray) -> float: - """ - Calculate cosine similarity between two image embeddings. - - Args: - embedding1: First image embedding - embedding2: Second image embedding - - Returns: - Similarity score (0-1, where 1 is most similar) - """ - if embedding1 is None or embedding2 is None: - return 0.0 - - # Ensure embeddings are normalized - norm1 = np.linalg.norm(embedding1) - norm2 = np.linalg.norm(embedding2) - - if norm1 == 0 or norm2 == 0: - return 0.0 - - embedding1_norm = embedding1 / norm1 - embedding2_norm = embedding2 / norm2 - - # Cosine similarity - similarity = np.dot(embedding1_norm, embedding2_norm) - - # Clip to [0, 1] range (cosine similarity is [-1, 1]) - similarity = (similarity + 1) / 2 - - return float(similarity) - - -def batch_generate_embeddings(image_urls: list[str]) -> list[Optional[np.ndarray]]: - """ - Generate embeddings for multiple images. - - Args: - image_urls: List of image URLs - - Returns: - List of embeddings (same length as input, None for failed downloads) - """ - generator = get_embedding_generator() - embeddings = [] - - for url in image_urls: - embedding = generator.generate_embedding_from_url(url) - embeddings.append(embedding) - - return embeddings - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/log_utils.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/log_utils.py deleted file mode 100644 index 68ddfac..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/log_utils.py +++ /dev/null @@ -1,46 +0,0 @@ -"""Logging utilities for Tuxedo Link.""" - -# Foreground colors -RED = '\033[31m' -GREEN = '\033[32m' -YELLOW = '\033[33m' -BLUE = '\033[34m' -MAGENTA = '\033[35m' -CYAN = '\033[36m' -WHITE = '\033[37m' - -# Background color -BG_BLACK = '\033[40m' -BG_BLUE = '\033[44m' - -# Reset code to return to default color -RESET = '\033[0m' - -# Mapping of terminal color codes to HTML colors -mapper = { - BG_BLACK+RED: "#dd0000", - BG_BLACK+GREEN: "#00dd00", - BG_BLACK+YELLOW: "#dddd00", - BG_BLACK+BLUE: "#0000ee", - BG_BLACK+MAGENTA: "#aa00dd", - BG_BLACK+CYAN: "#00dddd", - BG_BLACK+WHITE: "#87CEEB", - BG_BLUE+WHITE: "#ff7800" -} - - -def reformat(message: str) -> str: - """ - Convert terminal color codes to HTML spans for Gradio display. - - Args: - message: Log message with terminal color codes - - Returns: - HTML formatted message - """ - for key, value in mapper.items(): - message = message.replace(key, f'') - message = message.replace(RESET, '') - return message - diff --git a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/timing.py b/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/timing.py deleted file mode 100644 index 6628670..0000000 --- a/week8/community_contributions/dkisselev-zz/tuxedo_link/utils/timing.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Timing utilities for performance monitoring.""" - 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