Week8 dkisselev-zz update
This commit is contained in:
Dmitry Kisselev
834
week8/community_contributions/dkisselev-zz/tuxedo_link/app.py
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834
week8/community_contributions/dkisselev-zz/tuxedo_link/app.py
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@@ -0,0 +1,834 @@
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"""Gradio UI for Tuxedo Link cat adoption application."""
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import os
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import gradio as gr
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import pandas as pd
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from dotenv import load_dotenv
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from typing import List, Optional, Tuple
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import logging
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import re
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from datetime import datetime
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# Import models - these are lightweight
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from models.cats import CatProfile, CatMatch, AdoptionAlert
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from utils.config import is_production
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# Load environment
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load_dotenv()
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# Initialize framework based on mode
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framework = None
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profile_agent = None
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if not is_production():
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# LOCAL MODE: Import and initialize heavy components
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from cat_adoption_framework import TuxedoLinkFramework
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from agents.profile_agent import ProfileAgent
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framework = TuxedoLinkFramework()
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profile_agent = ProfileAgent()
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print("✓ Running in LOCAL mode - using local components")
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else:
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# PRODUCTION MODE: Don't import heavy components - use Modal API
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print("✓ Running in PRODUCTION mode - using Modal API")
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# Global state for current search results
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current_matches: List[CatMatch] = []
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current_profile: Optional[CatProfile] = None
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# Configure logging to suppress verbose output
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logging.getLogger().setLevel(logging.WARNING)
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def extract_profile_from_text(user_input: str, use_cache: bool = False) -> tuple:
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"""
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Extract structured profile from user's natural language input.
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Args:
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user_input: User's description of desired cat
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use_cache: Whether to use cached data for search
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Returns:
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Tuple of (chat_history, results_html, profile_json)
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"""
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global current_matches, current_profile
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try:
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# Handle empty input - use placeholder text
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if not user_input or user_input.strip() == "":
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user_input = "I'm looking for a friendly, playful kitten in NYC that's good with children"
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# Extract profile using LLM
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# Using messages format for Gradio chatbot
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chat_history = [
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{"role": "user", "content": user_input},
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{"role": "assistant", "content": "🔍 Analyzing your preferences..."}
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]
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# Extract profile (Modal or local)
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if is_production():
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# PRODUCTION: Call Modal API
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import modal
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# Look up deployed function - correct API!
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extract_profile_func = modal.Function.from_name("tuxedo-link-api", "extract_profile")
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print("[INFO] Calling Modal API to extract profile...")
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profile_result = extract_profile_func.remote(user_input)
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if not profile_result["success"]:
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return chat_history, "<p>❌ Error extracting profile</p>", "{}"
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profile = CatProfile(**profile_result["profile"])
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else:
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# LOCAL: Use local agent
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conversation = [{"role": "user", "content": user_input}]
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profile = profile_agent.extract_profile(conversation)
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current_profile = profile
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# Perform search
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response_msg = f"✅ Got it! Searching for:\n\n" + \
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f"📍 Location: {profile.user_location or 'Not specified'}\n" + \
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f"📏 Distance: {profile.max_distance or 100} miles\n" + \
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f"🎨 Colors: {', '.join(profile.color_preferences) if profile.color_preferences else 'Any'}\n" + \
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f"🎭 Personality: {profile.personality_description or 'Any'}\n" + \
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f"🎂 Age: {', '.join(profile.age_range) if profile.age_range else 'Any'}\n" + \
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f"👶 Good with children: {'Yes' if profile.good_with_children else 'Not required'}\n" + \
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f"🐕 Good with dogs: {'Yes' if profile.good_with_dogs else 'Not required'}\n" + \
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f"🐱 Good with cats: {'Yes' if profile.good_with_cats else 'Not required'}\n\n" + \
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f"Searching..."
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chat_history[1]["content"] = response_msg
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# Run search (Modal or local)
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if is_production():
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# PRODUCTION: Call Modal API
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import modal
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# Look up deployed function
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search_cats_func = modal.Function.from_name("tuxedo-link-api", "search_cats")
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print("[INFO] Calling Modal API to search cats...")
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search_result = search_cats_func.remote(profile.model_dump(), use_cache=use_cache)
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if not search_result["success"]:
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error_msg = search_result.get('error', 'Unknown error')
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chat_history.append({"role": "assistant", "content": f"❌ Search error: {error_msg}"})
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return chat_history, "<p>😿 Search failed. Please try again.</p>", profile.json()
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# Reconstruct matches from Modal response
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from models.cats import Cat
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current_matches = [
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CatMatch(
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cat=Cat(**m["cat"]),
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match_score=m["match_score"],
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vector_similarity=m["vector_similarity"],
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attribute_match_score=m["attribute_match_score"],
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explanation=m["explanation"],
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matching_attributes=m.get("matching_attributes", []),
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missing_attributes=m.get("missing_attributes", [])
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)
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for m in search_result["matches"]
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]
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else:
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# LOCAL: Use local framework
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result = framework.search(profile, use_cache=use_cache)
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current_matches = result.matches
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# Build results HTML
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if current_matches:
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chat_history[1]["content"] += f"\n\n✨ Found {len(current_matches)} great matches!"
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results_html = build_results_grid(current_matches)
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else:
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chat_history[1]["content"] += "\n\n😿 No matches found. Try broadening your search criteria."
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results_html = "<p style='text-align:center; color: #666; padding: 40px;'>No matches found</p>"
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# Profile JSON for display
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profile_json = profile.model_dump_json(indent=2)
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return chat_history, results_html, profile_json
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except Exception as e:
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error_msg = f"❌ Error: {str(e)}"
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print(f"[ERROR] Search failed: {e}")
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import traceback
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traceback.print_exc()
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return [
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{"role": "user", "content": user_input},
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{"role": "assistant", "content": error_msg}
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], "<p>Error occurred</p>", "{}"
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def build_results_grid(matches: List[CatMatch]) -> str:
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"""Build HTML grid of cat results."""
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html = "<div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(240px, 1fr)); gap: 20px; padding: 20px;'>"
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for match in matches:
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cat = match.cat
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photo = cat.primary_photo or "https://via.placeholder.com/240x180?text=No+Photo"
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html += f"""
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<div style='border: 1px solid #ddd; border-radius: 10px; overflow: hidden; box-shadow: 0 2px 8px rgba(0,0,0,0.1);'>
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<img src='{photo}' style='width: 100%; height: 180px; object-fit: cover;' onerror="this.src='https://via.placeholder.com/240x180?text=No+Photo'">
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<div style='padding: 15px;'>
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<h3 style='margin: 0 0 10px 0; color: #333;'>{cat.name}</h3>
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<div style='display: flex; justify-content: space-between; margin-bottom: 8px;'>
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<span style='background: #4CAF50; color: white; padding: 4px 12px; border-radius: 12px; font-size: 12px;'>
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{match.match_score:.0%} Match
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</span>
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<span style='color: #666; font-size: 14px;'>{cat.age}</span>
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</div>
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<p style='color: #666; font-size: 14px; margin: 8px 0;'>
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<strong>{cat.breed}</strong><br/>
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{cat.city}, {cat.state}<br/>
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{cat.gender.capitalize()} • {cat.size.capitalize() if cat.size else 'Unknown size'}
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</p>
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<p style='color: #888; font-size: 13px; margin: 10px 0; line-height: 1.4;'>
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{match.explanation}
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</p>
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<a href='{cat.url}' target='_blank' style='display: block; text-align: center; background: #2196F3; color: white; padding: 10px; border-radius: 5px; text-decoration: none; margin-top: 10px;'>
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View Details
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</a>
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</div>
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</div>
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"""
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html += "</div>"
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return html
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def search_with_examples(example_text: str, use_cache: bool = False) -> tuple:
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"""Handle example button clicks."""
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return extract_profile_from_text(example_text, use_cache)
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# ===== ALERT MANAGEMENT FUNCTIONS =====
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def validate_email(email: str) -> bool:
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"""Validate email address format."""
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pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
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return bool(re.match(pattern, email))
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def send_immediate_notification_local(alert_id: int) -> None:
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"""
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Send immediate notification locally (not via Modal).
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Args:
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alert_id: ID of the alert to process
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"""
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from agents.email_agent import EmailAgent
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from agents.email_providers.factory import get_email_provider
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print(f"[DEBUG] Sending immediate notification for alert {alert_id}")
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# Get alert from database
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alert = framework.db_manager.get_alert_by_id(alert_id)
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if not alert:
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print(f"[ERROR] Alert {alert_id} not found")
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raise ValueError(f"Alert {alert_id} not found")
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print(f"[DEBUG] Alert found: email={alert.user_email}, profile exists={alert.profile is not None}")
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# Run search with the alert's profile
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result = framework.search(alert.profile, use_cache=False)
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print(f"[DEBUG] Search complete: {len(result.matches)} matches found")
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if result.matches:
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# Send email notification
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try:
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email_provider = get_email_provider()
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email_agent = EmailAgent(email_provider)
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print(f"[DEBUG] Sending email to {alert.user_email}...")
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email_agent.send_match_notification(
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alert=alert,
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matches=result.matches
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)
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print(f"[DEBUG] ✓ Email sent successfully!")
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except Exception as e:
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print(f"[ERROR] Failed to send email: {e}")
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import traceback
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traceback.print_exc()
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raise
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else:
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print(f"[DEBUG] No matches found, no email sent")
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def save_alert(email: str, frequency: str, profile_json: str) -> Tuple[str, pd.DataFrame]:
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"""
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Save an adoption alert to the database.
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Args:
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email: User's email address
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frequency: Notification frequency (Immediately, Daily, Weekly)
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profile_json: JSON string of current search profile
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Returns:
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Tuple of (status_message, updated_alerts_dataframe)
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"""
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global current_profile
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try:
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# Validate email
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if not email or not validate_email(email):
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return "❌ Please enter a valid email address", load_alerts()
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# Check if we have a current profile
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if not current_profile:
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return "❌ Please perform a search first to create a profile", load_alerts()
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# Normalize frequency
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frequency = frequency.lower()
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# Create alert
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alert = AdoptionAlert(
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user_email=email,
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profile=current_profile,
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frequency=frequency,
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active=True
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)
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# Save alert based on mode
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if is_production():
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# PRODUCTION MODE: Use Modal function
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try:
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import modal
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print(f"[INFO] Production mode: Calling Modal function to create alert...")
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# Look up deployed function - correct API!
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create_alert_func = modal.Function.from_name("tuxedo-link-api", "create_alert_and_notify")
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# Send alert data to Modal
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result = create_alert_func.remote(alert.dict())
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if result["success"]:
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status = f"✅ {result['message']}"
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else:
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status = f"⚠️ {result['message']}"
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return status, load_alerts()
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except Exception as e:
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import traceback
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error_detail = traceback.format_exc()
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print(f"[ERROR] Modal function failed: {error_detail}")
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return f"❌ Error calling Modal service: {str(e)}\n\nCheck Modal logs for details.", load_alerts()
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else:
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# LOCAL MODE: Save and process locally
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alert_id = framework.db_manager.create_alert(alert)
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if frequency == "immediately":
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try:
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send_immediate_notification_local(alert_id)
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status = f"✅ Alert saved and notification sent locally! (ID: {alert_id})\n\nCheck your email at {email}"
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except Exception as e:
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import traceback
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error_detail = traceback.format_exc()
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print(f"[ERROR] Local notification failed: {error_detail}")
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status = f"✅ Alert saved (ID: {alert_id}), but notification failed: {str(e)}"
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else:
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status = f"✅ Alert saved successfully! (ID: {alert_id})\n\nYou'll receive {frequency} notifications at {email}"
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return status, load_alerts()
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except Exception as e:
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return f"❌ Error saving alert: {str(e)}", load_alerts()
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def load_alerts(email_filter: str = "") -> pd.DataFrame:
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"""
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Load all alerts from the database.
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Args:
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email_filter: Optional email to filter by
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Returns:
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DataFrame of alerts
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"""
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try:
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# Get alerts from database (Modal or local)
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if is_production():
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# PRODUCTION: Call Modal API
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import modal
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# Look up deployed function
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get_alerts_func = modal.Function.from_name("tuxedo-link-api", "get_alerts")
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alert_dicts = get_alerts_func.remote(email=email_filter if email_filter and validate_email(email_filter) else None)
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alerts = [AdoptionAlert(**a) for a in alert_dicts]
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else:
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# LOCAL: Use local database
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if email_filter and validate_email(email_filter):
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alerts = framework.db_manager.get_alerts_by_email(email_filter)
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else:
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alerts = framework.db_manager.get_all_alerts()
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if not alerts:
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# Return empty DataFrame with correct columns
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return pd.DataFrame(columns=["ID", "Email", "Frequency", "Location", "Preferences", "Last Sent", "Status"])
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# Convert to display format
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data = []
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for alert in alerts:
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location = alert.profile.user_location or "Any"
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prefs = []
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if alert.profile.age_range:
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prefs.append(f"Age: {', '.join(alert.profile.age_range)}")
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if alert.profile.good_with_children:
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prefs.append("Child-friendly")
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if alert.profile.good_with_dogs:
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prefs.append("Dog-friendly")
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if alert.profile.good_with_cats:
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prefs.append("Cat-friendly")
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prefs_str = ", ".join(prefs) if prefs else "Any"
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last_sent = alert.last_sent.strftime("%Y-%m-%d %H:%M") if alert.last_sent else "Never"
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status = "🟢 Active" if alert.active else "🔴 Inactive"
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data.append({
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"ID": alert.id,
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"Email": alert.user_email,
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"Frequency": alert.frequency.capitalize(),
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"Location": location,
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"Preferences": prefs_str,
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"Last Sent": last_sent,
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"Status": status
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})
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return pd.DataFrame(data)
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except Exception as e:
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logging.error(f"Error loading alerts: {e}")
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return pd.DataFrame(columns=["ID", "Email", "Frequency", "Location", "Preferences", "Last Sent", "Status"])
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def delete_alert(alert_id: str, email_filter: str = "") -> Tuple[str, pd.DataFrame]:
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"""
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Delete an alert by ID.
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Args:
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alert_id: Alert ID to delete
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email_filter: Optional email filter for refresh
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Returns:
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Tuple of (status_message, updated_alerts_dataframe)
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"""
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try:
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if not alert_id:
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return "❌ Please enter an Alert ID", load_alerts(email_filter)
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# Convert to int
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try:
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alert_id_int = int(alert_id)
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except ValueError:
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return f"❌ Invalid Alert ID: {alert_id}", load_alerts(email_filter)
|
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# Delete from database (Modal or local)
|
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if is_production():
|
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# PRODUCTION: Call Modal API
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import modal
|
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|
||||
# Look up deployed function
|
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delete_alert_func = modal.Function.from_name("tuxedo-link-api", "delete_alert")
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success = delete_alert_func.remote(alert_id_int)
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if not success:
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return f"❌ Failed to delete alert {alert_id}", load_alerts(email_filter)
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else:
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# LOCAL: Use local database
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framework.db_manager.delete_alert(alert_id_int)
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return f"✅ Alert {alert_id} deleted successfully", load_alerts(email_filter)
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except Exception as e:
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return f"❌ Error deleting alert: {str(e)}", load_alerts(email_filter)
|
||||
|
||||
|
||||
def toggle_alert_status(alert_id: str, email_filter: str = "") -> Tuple[str, pd.DataFrame]:
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"""
|
||||
Toggle alert active/inactive status.
|
||||
|
||||
Args:
|
||||
alert_id: Alert ID to toggle
|
||||
email_filter: Optional email filter for refresh
|
||||
|
||||
Returns:
|
||||
Tuple of (status_message, updated_alerts_dataframe)
|
||||
"""
|
||||
try:
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||||
if not alert_id:
|
||||
return "❌ Please enter an Alert ID", load_alerts(email_filter)
|
||||
|
||||
# Convert to int
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||||
try:
|
||||
alert_id_int = int(alert_id)
|
||||
except ValueError:
|
||||
return f"❌ Invalid Alert ID: {alert_id}", load_alerts(email_filter)
|
||||
|
||||
# Get current alert and toggle (Modal or local)
|
||||
if is_production():
|
||||
# PRODUCTION: Call Modal API
|
||||
import modal
|
||||
|
||||
# Look up deployed functions
|
||||
get_alerts_func = modal.Function.from_name("tuxedo-link-api", "get_alerts")
|
||||
update_alert_func = modal.Function.from_name("tuxedo-link-api", "update_alert")
|
||||
|
||||
# Get all alerts and find this one
|
||||
alert_dicts = get_alerts_func.remote()
|
||||
alert_dict = next((a for a in alert_dicts if a["id"] == alert_id_int), None)
|
||||
|
||||
if not alert_dict:
|
||||
return f"❌ Alert {alert_id} not found", load_alerts(email_filter)
|
||||
|
||||
alert = AdoptionAlert(**alert_dict)
|
||||
new_status = not alert.active
|
||||
|
||||
success = update_alert_func.remote(alert_id_int, active=new_status)
|
||||
if not success:
|
||||
return f"❌ Failed to update alert {alert_id}", load_alerts(email_filter)
|
||||
else:
|
||||
# LOCAL: Use local database
|
||||
alert = framework.db_manager.get_alert(alert_id_int)
|
||||
if not alert:
|
||||
return f"❌ Alert {alert_id} not found", load_alerts(email_filter)
|
||||
|
||||
new_status = not alert.active
|
||||
framework.db_manager.update_alert(alert_id_int, active=new_status)
|
||||
|
||||
status_text = "activated" if new_status else "deactivated"
|
||||
return f"✅ Alert {alert_id} {status_text}", load_alerts(email_filter)
|
||||
|
||||
except Exception as e:
|
||||
return f"❌ Error toggling alert: {str(e)}", load_alerts(email_filter)
|
||||
|
||||
|
||||
def build_search_tab() -> None:
|
||||
"""Build the search tab interface with chat and results display."""
|
||||
with gr.Column():
|
||||
gr.Markdown("# 🐱 Find Your Perfect Cat")
|
||||
gr.Markdown("Tell me what kind of cat you're looking for, and I'll help you find the perfect match!")
|
||||
|
||||
with gr.Row():
|
||||
# In production mode, default to False since Modal cache starts empty
|
||||
# In local mode, can default to True after first run
|
||||
default_cache = False if is_production() else True
|
||||
use_cache_checkbox = gr.Checkbox(
|
||||
label="Use Cache (Fast Mode)",
|
||||
value=default_cache,
|
||||
info="Use cached cat data for faster searches (uncheck for fresh data from APIs)"
|
||||
)
|
||||
|
||||
# Chat interface for natural language input
|
||||
chatbot = gr.Chatbot(label="Chat", height=200, type="messages")
|
||||
user_input = gr.Textbox(
|
||||
label="Describe your ideal cat",
|
||||
placeholder="I'm looking for a friendly, playful kitten in NYC that's good with children...",
|
||||
lines=3
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
submit_btn = gr.Button("🔍 Search", variant="primary")
|
||||
clear_btn = gr.Button("🔄 Clear")
|
||||
|
||||
# Example queries
|
||||
gr.Markdown("### 💡 Try these examples:")
|
||||
with gr.Row():
|
||||
example_btns = [
|
||||
gr.Button("🏠 Family cat", size="sm"),
|
||||
gr.Button("🎮 Playful kitten", size="sm"),
|
||||
gr.Button("😴 Calm adult", size="sm"),
|
||||
gr.Button("👶 Good with kids", size="sm")
|
||||
]
|
||||
|
||||
# Results display
|
||||
gr.Markdown("---")
|
||||
gr.Markdown("## 🎯 Search Results")
|
||||
results_html = gr.HTML(value="<p style='text-align:center; color: #999; padding: 40px;'>Enter your preferences above to start searching</p>")
|
||||
|
||||
# Profile display (collapsible)
|
||||
with gr.Accordion("📋 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: ([], "<p style='text-align:center; color: #999; padding: 40px;'>Enter your preferences above to start searching</p>", ""),
|
||||
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("# 🔔 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("### 💾 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("💾 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("### 📋 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("🔄 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("### ⚙️ 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("🔄 Toggle Active/Inactive", size="sm")
|
||||
delete_btn = gr.Button("🗑️ 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("# 🎩 About Tuxedo Link")
|
||||
|
||||
gr.Markdown("""
|
||||
## In Loving Memory of Kyra 🐱
|
||||
|
||||
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 cat—
|
||||
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
|
||||
|
||||
✅ **Multi-Platform Search**: Petfinder, RescueGroups
|
||||
✅ **AI-Powered Matching**: Semantic search with vector embeddings
|
||||
✅ **Smart Deduplication**: Name, description, and image similarity
|
||||
✅ **Personality Matching**: Find cats that match your lifestyle
|
||||
✅ **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 💙
|
||||
|
||||
---
|
||||
|
||||
*"In memory of Kyra, who taught us that home is wherever your cat is."*
|
||||
|
||||
🐾 **May every cat find their perfect home** 🐾
|
||||
""")
|
||||
|
||||
# Add Kyra's picture
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
gr.Image(
|
||||
value="assets/Kyra.png",
|
||||
label="Kyra - Forever in our hearts 💙",
|
||||
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("""
|
||||
<div style='text-align: center; padding: 20px;'>
|
||||
<h1 style='font-size: 3em; margin: 0;'>🎩 Tuxedo Link</h1>
|
||||
<p style='font-size: 1.2em; color: #666; margin: 10px 0;'>
|
||||
AI-Powered Cat Adoption Search
|
||||
</p>
|
||||
</div>
|
||||
""")
|
||||
|
||||
with gr.Tabs():
|
||||
with gr.Tab("🔍 Search"):
|
||||
build_search_tab()
|
||||
|
||||
with gr.Tab("🔔 Alerts"):
|
||||
build_alerts_tab()
|
||||
|
||||
with gr.Tab("ℹ️ About"):
|
||||
build_about_tab()
|
||||
|
||||
gr.Markdown("""
|
||||
<div style='text-align: center; padding: 20px; color: #999; font-size: 0.9em;'>
|
||||
Made with ❤️ in memory of Kyra |
|
||||
<a href='https://github.com/yourusername/tuxedo-link' style='color: #2196F3;'>GitHub</a> |
|
||||
Powered by AI & Open Source
|
||||
</div>
|
||||
""")
|
||||
|
||||
return app
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app = create_app()
|
||||
app.launch(
|
||||
server_name="0.0.0.0",
|
||||
server_port=7860,
|
||||
share=False,
|
||||
show_error=True
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user