diff --git a/week3/community-contributions/Week3-Dataset_Generator-DP.ipynb b/week3/community-contributions/Week3-Dataset_Generator-DP.ipynb new file mode 100644 index 0000000..72c1c84 --- /dev/null +++ b/week3/community-contributions/Week3-Dataset_Generator-DP.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "c08309b8-13f0-45bb-a3ea-7b01f05a7346", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import pandas as pd\n", + "import random\n", + "import re\n", + "import subprocess\n", + "import pyarrow as pa\n", + "from typing import List\n", + "import openai\n", + "import anthropic\n", + "from dotenv import load_dotenv\n", + "import gradio as gr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5efd903-e683-4e7f-8747-2998e23a0751", + "metadata": {}, + "outputs": [], + "source": [ + "# load API\n", + "load_dotenv(override=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce49b86a-53f4-4d4f-a721-0d66d9c1b070", + "metadata": {}, + "outputs": [], + "source": [ + "# --- Schema Definition ---\n", + "SCHEMA = [\n", + " (\"Team\", \"TEXT\", '\"Toronto Raptors\"'),\n", + " (\"NAME\", \"TEXT\", '\"Otto Porter Jr.\"'),\n", + " (\"Jersey\", \"TEXT\", '\"10\", or \"NA\" if null'),\n", + " (\"POS\", \"TEXT\", 'One of [\"PF\",\"SF\",\"G\",\"C\",\"SG\",\"F\",\"PG\"]'),\n", + " (\"AGE\", \"INT\", 'integer age in years, e.g., 22'),\n", + " (\"HT\", \"TEXT\", '`6\\' 7\"` or `6\\' 10\"`'),\n", + " (\"WT\", \"TEXT\", '\"232 lbs\"'),\n", + " (\"COLLEGE\", \"TEXT\", '\"Michigan\", or \"--\" if null'),\n", + " (\"SALARY\", \"TEXT\", '\"$9,945,830\", or \"--\" if null')\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93743e57-c2c5-43e5-8fa1-2e242085db07", + "metadata": {}, + "outputs": [], + "source": [ + "# Default schema text for the textbox\n", + "DEFAULT_SCHEMA_TEXT = \"\\n\".join([f\"{i+1}. {col[0]} ({col[1]}) Example: {col[2]}\" for i, col in enumerate(SCHEMA)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87c58595-6fdd-48f5-a253-ccba352cb385", + "metadata": {}, + "outputs": [], + "source": [ + "# Available models\n", + "MODELS = [\n", + " \"gpt-4o\",\n", + " \"claude-3-5-haiku-20241022\", \n", + " \"ollama:llama3.2:latest\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08cd9ce2-8685-46b5-95d0-811b8025696f", + "metadata": {}, + "outputs": [], + "source": [ + "# Available file formats\n", + "FILE_FORMATS = [\".csv\", \".tsv\", \".jsonl\", \".parquet\", \".arrow\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13d68c7f-6f49-4efa-b075-f1e7db2ab527", + "metadata": {}, + "outputs": [], + "source": [ + "def get_prompt(n: int, schema_text: str, system_prompt: str) -> str:\n", + " prompt = f\"\"\"\n", + "{system_prompt}\n", + "\n", + "Generate {n} rows of realistic basketball player data in JSONL format, each line a JSON object with the following fields:\n", + "\n", + "{schema_text}\n", + "\n", + "Do NOT repeat column values from one row to another.\n", + "\n", + "Only output valid JSONL.\n", + "\"\"\"\n", + " return prompt.strip()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdc68f1e-4fbe-45dc-aa36-ce5f718ef6ca", + "metadata": {}, + "outputs": [], + "source": [ + "# --- LLM Interface ---\n", + "def query_model(prompt: str, model: str = \"gpt-4o\") -> List[dict]:\n", + " \"\"\"Call OpenAI, Claude, or Ollama\"\"\"\n", + " try:\n", + " if model.lower().startswith(\"gpt\"):\n", + " client = openai.OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n", + " response = client.chat.completions.create(\n", + " model=model,\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + " temperature=0.7\n", + " )\n", + " content = response.choices[0].message.content\n", + "\n", + " elif model.lower().startswith(\"claude\"):\n", + " client = anthropic.Anthropic(api_key=os.getenv(\"ANTHROPIC_API_KEY\"))\n", + " response = client.messages.create(\n", + " model=model,\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + " max_tokens=4000,\n", + " temperature=0.7\n", + " )\n", + " content = response.content[0].text\n", + "\n", + " elif model.lower().startswith(\"ollama:\"):\n", + " ollama_model = model.split(\":\")[1]\n", + " result = subprocess.run(\n", + " [\"ollama\", \"run\", ollama_model],\n", + " input=prompt,\n", + " text=True,\n", + " capture_output=True\n", + " )\n", + " if result.returncode != 0:\n", + " raise Exception(f\"Ollama error: {result.stderr}\")\n", + " content = result.stdout\n", + " else:\n", + " raise ValueError(\"Unsupported model. Use 'gpt-4.1-mini', 'claude-3-5-haiku-20241022', or 'ollama:llama3.2:latest'\")\n", + "\n", + " # Parse JSONL output\n", + " lines = [line.strip() for line in content.strip().splitlines() if line.strip().startswith(\"{\")]\n", + " return [json.loads(line) for line in lines]\n", + " \n", + " except Exception as e:\n", + " raise Exception(f\"Model query failed: {str(e)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29e3f5f5-e99c-429c-bea9-69d554c58c9c", + "metadata": {}, + "outputs": [], + "source": [ + "# --- Output Formatter ---\n", + "def save_dataset(records: List[dict], file_format: str, filename: str):\n", + " df = pd.DataFrame(records)\n", + " if file_format == \".csv\":\n", + " df.to_csv(filename, index=False)\n", + " elif file_format == \".tsv\":\n", + " df.to_csv(filename, sep=\"\\t\", index=False)\n", + " elif file_format == \".jsonl\":\n", + " with open(filename, \"w\") as f:\n", + " for record in records:\n", + " f.write(json.dumps(record) + \"\\n\")\n", + " elif file_format == \".parquet\":\n", + " df.to_parquet(filename, engine=\"pyarrow\", index=False)\n", + " elif file_format == \".arrow\":\n", + " table = pa.Table.from_pandas(df)\n", + " with pa.OSFile(filename, \"wb\") as sink:\n", + " with pa.ipc.new_file(sink, table.schema) as writer:\n", + " writer.write(table)\n", + " else:\n", + " raise ValueError(\"Unsupported file format\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe258e84-66f4-4fe7-99c0-75b24148e147", + "metadata": {}, + "outputs": [], + "source": [ + "# --- Main Generation Function ---\n", + "def generate_dataset(schema_text, system_prompt, model, nr_records, file_format, save_as):\n", + " try:\n", + " # Validation\n", + " if nr_records <= 10:\n", + " return \"āŒ Error: Nr_records must be greater than 10.\", None\n", + " \n", + " if file_format not in FILE_FORMATS:\n", + " return \"āŒ Error: Invalid file format specified.\", None\n", + " \n", + " if not save_as or save_as.strip() == \"\":\n", + " save_as = f\"basketball_dataset{file_format}\"\n", + " elif not save_as.endswith(file_format):\n", + " save_as = save_as + file_format\n", + " \n", + " # Generate prompt\n", + " prompt = get_prompt(nr_records, schema_text, system_prompt)\n", + " \n", + " # Query model\n", + " records = query_model(prompt, model=model)\n", + " \n", + " if not records:\n", + " return \"āŒ Error: No valid records generated from the model.\", None\n", + " \n", + " # Save dataset\n", + " save_dataset(records, file_format, save_as)\n", + " \n", + " # Create preview\n", + " df = pd.DataFrame(records)\n", + " preview = df.head(10) # Show first 10 rows\n", + " \n", + " success_message = f\"āœ… Dataset generated successfully!\\nšŸ“ Saved to: {save_as}\\nšŸ“Š Generated {len(records)} records\"\n", + " \n", + " return success_message, preview\n", + " \n", + " except Exception as e:\n", + " return f\"āŒ Error: {str(e)}\", None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2405a9d-b4cd-43d9-82f6-ff3512b4541f", + "metadata": {}, + "outputs": [], + "source": [ + "# --- Gradio Interface ---\n", + "def create_interface():\n", + " with gr.Blocks(title=\"Dataset Generator\", theme=gr.themes.Soft()) as interface:\n", + " gr.Markdown(\"# Dataset Generator\")\n", + " gr.Markdown(\"Generate realistic datasets using AI models\")\n", + " \n", + " with gr.Row():\n", + " with gr.Column(scale=2):\n", + " schema_input = gr.Textbox(\n", + " label=\"Schema\",\n", + " value=DEFAULT_SCHEMA_TEXT,\n", + " lines=15,\n", + " placeholder=\"Define your dataset schema here...\"\n", + " )\n", + " \n", + " system_prompt_input = gr.Textbox(\n", + " label=\"Prompt\",\n", + " value=\"You are a helpful assistant that generates realistic basketball player data.\",\n", + " lines=1,\n", + " placeholder=\"Enter system prompt for the model...\"\n", + " )\n", + " \n", + " with gr.Row():\n", + " model_dropdown = gr.Dropdown(\n", + " label=\"Model\",\n", + " choices=MODELS,\n", + " value=MODELS[1], # Default to Claude\n", + " interactive=True\n", + " )\n", + " \n", + " nr_records_input = gr.Number(\n", + " label=\"Nr. records\",\n", + " value=25,\n", + " minimum=11,\n", + " maximum=1000,\n", + " step=1\n", + " )\n", + " \n", + " with gr.Row():\n", + " file_format_dropdown = gr.Dropdown(\n", + " label=\"File format\",\n", + " choices=FILE_FORMATS,\n", + " value=\".csv\",\n", + " interactive=True\n", + " )\n", + " \n", + " save_as_input = gr.Textbox(\n", + " label=\"Save as\",\n", + " value=\"basketball_dataset\",\n", + " placeholder=\"Enter filename (extension will be added automatically)\"\n", + " )\n", + " \n", + " generate_btn = gr.Button(\"šŸš€ Generate\", variant=\"primary\", size=\"lg\")\n", + " \n", + " with gr.Column(scale=1):\n", + " output_status = gr.Textbox(\n", + " label=\"Status\",\n", + " lines=4,\n", + " interactive=False\n", + " )\n", + " \n", + " output_preview = gr.Dataframe(\n", + " label=\"Preview (First 10 rows)\",\n", + " interactive=False,\n", + " wrap=True\n", + " )\n", + " \n", + " # Connect the generate button\n", + " generate_btn.click(\n", + " fn=generate_dataset,\n", + " inputs=[\n", + " schema_input,\n", + " system_prompt_input, \n", + " model_dropdown,\n", + " nr_records_input,\n", + " file_format_dropdown,\n", + " save_as_input\n", + " ],\n", + " outputs=[output_status, output_preview]\n", + " )\n", + " \n", + " gr.Markdown(\"\"\"\n", + " ### šŸ“ Instructions:\n", + " 1. **Schema**: Define the structure of your dataset (pre-filled with basketball player schema)\n", + " 2. **Prompt**: System prompt to guide the AI model\n", + " 3. **Model**: Choose between GPT, Claude, or Ollama models\n", + " 4. **Nr. records**: Number of records to generate (minimum 11)\n", + " 5. **File format**: Choose output format (.csv, .tsv, .jsonl, .parquet, .arrow)\n", + " 6. **Save as**: Filename (extension added automatically)\n", + " 7. Click **Generate** to create your dataset\n", + " \n", + " ### šŸ”§ Requirements:\n", + " - Set up your API keys in `.env` file (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`)\n", + " - For Ollama models, ensure Ollama is installed and running locally\n", + " \"\"\")\n", + " \n", + " return interface" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50fd2b91-2578-4224-b9dd-e28caf6a0a85", + "metadata": {}, + "outputs": [], + "source": [ + "interface = create_interface()\n", + "interface.launch(inbrowser=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/week4/community-contributions/Week4-Comments-Generator-DP.ipynb b/week4/community-contributions/Week4-Comments-Generator-DP.ipynb new file mode 100644 index 0000000..6b3b698 --- /dev/null +++ b/week4/community-contributions/Week4-Comments-Generator-DP.ipynb @@ -0,0 +1,400 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "3e473bbd-a0c2-43bd-bf99-c749784d00c3", + "metadata": {}, + "outputs": [], + "source": [ + "import gradio as gr\n", + "import openai\n", + "import anthropic\n", + "import google.generativeai as genai\n", + "import requests\n", + "import json\n", + "import os\n", + "from typing import Dict, Any, Optional\n", + "import asyncio\n", + "from dotenv import load_dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16210512-41f1-4de3-8348-2cd7129e023f", + "metadata": {}, + "outputs": [], + "source": [ + "# load API\n", + "load_dotenv(override=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6747e275-91eb-4d2b-90b6-805f2bd9b6b7", + "metadata": {}, + "outputs": [], + "source": [ + "class CodeCommenter:\n", + " def __init__(self):\n", + " # Initialize API clients\n", + " self.openai_client = None\n", + " self.anthropic_client = None\n", + " self.gemini_client = None\n", + " \n", + " # Load API keys from environment variables\n", + " self.setup_clients()\n", + " \n", + " def setup_clients(self):\n", + " \"\"\"Initialize API clients with keys from environment variables\"\"\"\n", + " try:\n", + " # OpenAI\n", + " openai_key = os.getenv('OPENAI_API_KEY')\n", + " if openai_key:\n", + " self.openai_client = openai.OpenAI(api_key=openai_key)\n", + " \n", + " # Anthropic\n", + " anthropic_key = os.getenv('ANTHROPIC_API_KEY')\n", + " if anthropic_key:\n", + " self.anthropic_client = anthropic.Anthropic(api_key=anthropic_key)\n", + " \n", + " # Google Gemini\n", + " gemini_key = os.getenv('GOOGLE_API_KEY')\n", + " if gemini_key:\n", + " genai.configure(api_key=gemini_key)\n", + " self.gemini_client = genai.GenerativeModel('gemini-2.0-flash-exp')\n", + " \n", + " except Exception as e:\n", + " print(f\"Warning: Error setting up API clients: {e}\")\n", + " \n", + " def create_prompt(self, code: str, language: str) -> str:\n", + " \"\"\"Create a prompt for the LLM to add comments and docstrings\"\"\"\n", + " return f\"\"\"Please add detailed and helpful comments and docstrings to the following {language} code. \n", + " \n", + "Guidelines:\n", + "1. Add comprehensive docstrings for functions, classes, and modules\n", + "2. Add inline comments explaining complex logic\n", + "3. Follow the commenting conventions for {language}\n", + "4. Maintain the original code structure and functionality\n", + "5. Make comments clear and professional\n", + "6. Don't change the actual code logic, only add comments\n", + "7. Do not add code markdown delimiters like ```python\n", + "\n", + "Here's the code to comment:\n", + "\n", + "{code}\n", + "\n", + "Please return only the commented code without any additional explanation or markdown formatting.\"\"\"\n", + "\n", + " def call_openai(self, prompt: str, model: str = \"gpt-4o-mini\") -> str:\n", + " \"\"\"Make API call to OpenAI\"\"\"\n", + " if not self.openai_client:\n", + " return \"Error: OpenAI API key not configured. Please set OPENAI_API_KEY environment variable.\"\n", + " \n", + " try:\n", + " response = self.openai_client.chat.completions.create(\n", + " model=model,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful coding assistant that adds detailed comments and docstrings to code.\"},\n", + " {\"role\": \"user\", \"content\": prompt}\n", + " ],\n", + " max_tokens=4000,\n", + " temperature=0.1\n", + " )\n", + " return response.choices[0].message.content.strip()\n", + " except Exception as e:\n", + " return f\"Error calling OpenAI API: {str(e)}\"\n", + " \n", + " def call_anthropic(self, prompt: str, model: str = \"claude-3-5-haiku-20241022\") -> str:\n", + " \"\"\"Make API call to Anthropic Claude\"\"\"\n", + " if not self.anthropic_client:\n", + " return \"Error: Anthropic API key not configured. Please set ANTHROPIC_API_KEY environment variable.\"\n", + " \n", + " try:\n", + " response = self.anthropic_client.messages.create(\n", + " model=model,\n", + " max_tokens=4000,\n", + " temperature=0.1,\n", + " messages=[\n", + " {\"role\": \"user\", \"content\": prompt}\n", + " ]\n", + " )\n", + " return response.content[0].text.strip()\n", + " except Exception as e:\n", + " return f\"Error calling Anthropic API: {str(e)}\"\n", + " \n", + " def call_gemini(self, prompt: str) -> str:\n", + " \"\"\"Make API call to Google Gemini\"\"\"\n", + " if not self.gemini_client:\n", + " return \"Error: Google API key not configured. Please set GOOGLE_API_KEY environment variable.\"\n", + " \n", + " try:\n", + " response = self.gemini_client.generate_content(\n", + " prompt,\n", + " generation_config=genai.types.GenerationConfig(\n", + " max_output_tokens=4000,\n", + " temperature=0.1,\n", + " )\n", + " )\n", + " return response.text.strip()\n", + " except Exception as e:\n", + " return f\"Error calling Gemini API: {str(e)}\"\n", + " \n", + " def call_ollama(self, prompt: str, model: str = \"llama3.2:latest\") -> str:\n", + " \"\"\"Make API call to Ollama (local)\"\"\"\n", + " try:\n", + " url = \"http://localhost:11434/api/generate\"\n", + " data = {\n", + " \"model\": model,\n", + " \"prompt\": prompt,\n", + " \"stream\": False,\n", + " \"options\": {\n", + " \"temperature\": 0.1,\n", + " \"num_predict\": 4000\n", + " }\n", + " }\n", + " \n", + " response = requests.post(url, json=data, timeout=60)\n", + " if response.status_code == 200:\n", + " result = response.json()\n", + " return result.get('response', '').strip()\n", + " else:\n", + " return f\"Error calling Ollama API: HTTP {response.status_code}\"\n", + " except requests.exceptions.ConnectionError:\n", + " return \"Error: Could not connect to Ollama. Make sure Ollama is running locally on port 11434.\"\n", + " except Exception as e:\n", + " return f\"Error calling Ollama API: {str(e)}\"\n", + "\n", + " def generate_comments(self, language: str, code: str, llm: str) -> str:\n", + " \"\"\"Generate comments for the given code using the specified LLM\"\"\"\n", + " if not code.strip():\n", + " return \"Error: Please provide code to comment.\"\n", + " \n", + " prompt = self.create_prompt(code, language)\n", + " \n", + " # Route to appropriate LLM\n", + " if llm == \"gpt-4o-mini\":\n", + " return self.call_openai(prompt, \"gpt-4o-mini\")\n", + " elif llm == \"claude-3-5-haiku-20241022\":\n", + " return self.call_anthropic(prompt, \"claude-3-5-haiku-20241022\")\n", + " elif llm == \"gemini-2.0-flash\":\n", + " return self.call_gemini(prompt)\n", + " elif llm == \"ollama:llama3.2:latest\":\n", + " return self.call_ollama(prompt, \"llama3.2:latest\")\n", + " else:\n", + " return f\"Error: Unsupported LLM: {llm}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "813f0911-d53f-4887-9341-656712e32d8f", + "metadata": {}, + "outputs": [], + "source": [ + "def create_gradio_interface():\n", + " \"\"\"Create and configure the Gradio interface\"\"\"\n", + " commenter = CodeCommenter()\n", + " \n", + " # Define the main function for the interface\n", + " def process_code(language, code, llm):\n", + " \"\"\"Process the code and return commented version\"\"\"\n", + " if not code.strip():\n", + " return \"Please enter some code to comment.\"\n", + " \n", + " # Show processing message\n", + " processing_msg = f\"Processing {language} code with {llm}...\"\n", + " print(processing_msg)\n", + " \n", + " # Generate comments\n", + " result = commenter.generate_comments(language, code, llm)\n", + " return result\n", + " \n", + " # Define default code\n", + " default_code = \"\"\"import pyodbc\n", + "from tabulate import tabulate\n", + "def connect_to_sql_server(server_name, database, username=None, password=None):\n", + " try:\n", + " if username and password:\n", + " connection_string = f\"DRIVER={{ODBC Driver 17 for SQL Server}};SERVER={server_name};DATABASE={database};UID={username};PWD={password}\"\n", + " else:\n", + " connection_string = f\"DRIVER={{ODBC Driver 17 for SQL Server}};SERVER={server_name};DATABASE={database};Trusted_Connection=yes\"\n", + " connection = pyodbc.connect(connection_string)\n", + " print(f\"Successfully connected to {server_name}/{database}\")\n", + " return connection\n", + " except Exception as e:\n", + " print(f\"Failed to connect to {server_name}/{database}: {str(e)}\")\n", + " return None\n", + "def get_record_count(connection, table_name):\n", + " try:\n", + " cursor = connection.cursor()\n", + " query = f\"SELECT COUNT(*) FROM {table_name}\"\n", + " cursor.execute(query)\n", + " count = cursor.fetchone()[0]\n", + " cursor.close()\n", + " print(f\"Record count for {table_name}: {count}\")\n", + " return count\n", + " except Exception as e:\n", + " print(f\"Failed to get record count for {table_name}: {str(e)}\")\n", + " return None\n", + "def select_top_records(connection, table_name, n):\n", + " try:\n", + " cursor = connection.cursor()\n", + " query = f\"SELECT TOP {n} * FROM {table_name}\"\n", + " cursor.execute(query)\n", + " records = cursor.fetchall()\n", + " columns = [column[0] for column in cursor.description]\n", + " cursor.close()\n", + " print(f\"Top {n} records from {table_name}\")\n", + " if records:\n", + " print(tabulate(records, headers=columns, tablefmt=\"grid\"))\n", + " return records\n", + " except Exception as e:\n", + " print(f\"Failed to retrieve top {n} records from {table_name}: {str(e)}\")\n", + " return None\n", + "conn = connect_to_sql_server(\"localhost\", \"AdventureWorks_lite\")\n", + "if conn:\n", + " total_records = get_record_count(conn, \"Sales.SalesOrderDetail\")\n", + " top_records = select_top_records(conn, \"Production.Product\", 10)\n", + " conn.close()\n", + " print(\"Connection closed successfully\")\"\"\"\n", + "\n", + " css = \"\"\"\n", + "textarea[rows]:not([rows=\"1\"]) {\n", + " overflow-y: auto !important;\n", + " scrollbar-width: thin !important;\n", + "}\n", + "textarea[rows]:not([rows=\"1\"])::-webkit-scrollbar {\n", + " all: initial !important;\n", + " background: #f1f1f1 !important;\n", + "}\n", + "textarea[rows]:not([rows=\"1\"])::-webkit-scrollbar-thumb {\n", + " all: initial !important;\n", + " background: #a8a8a8 !important;\n", + "}\n", + "\"\"\"\n", + "\n", + " # Create the interface\n", + " with gr.Blocks(title=\"Code Commenter\", theme=gr.themes.Base(), css=css) as interface:\n", + " gr.Markdown(\"# šŸ”§ Code Commenter\")\n", + " gr.Markdown(\"Add detailed comments and docstrings to your code using various LLM models.\")\n", + " \n", + " with gr.Row():\n", + " with gr.Column():\n", + " code_input = gr.Textbox(\n", + " label=\"Input Code\",\n", + " value=default_code,\n", + " lines=15,\n", + " max_lines=20,\n", + " info=\"Enter the code you want to add comments to\"\n", + " )\n", + " \n", + " with gr.Column():\n", + " code_output = gr.Textbox(\n", + " label=\"Commented Code\",\n", + " lines=20,\n", + " max_lines=20,\n", + " info=\"Your code with added comments and docstrings\"\n", + " )\n", + " \n", + " with gr.Row():\n", + " with gr.Column(scale=1):\n", + " language_dropdown = gr.Dropdown(\n", + " choices=[\"Python\", \"Ruby\", \"Rust\", \"C++\", \"Java\"],\n", + " value=\"Python\",\n", + " label=\"Programming Language\",\n", + " info=\"Select the programming language of your code\"\n", + " )\n", + " \n", + " llm_dropdown = gr.Dropdown(\n", + " choices=[\n", + " \"gpt-4o-mini\",\n", + " \"claude-3-5-haiku-20241022\", \n", + " \"gemini-2.0-flash\",\n", + " \"ollama:llama3.2:latest\"\n", + " ],\n", + " value=\"gpt-4o-mini\",\n", + " label=\"LLM Model\",\n", + " info=\"Choose the language model to use\"\n", + " )\n", + " \n", + " generate_btn = gr.Button(\n", + " \"šŸš€ Generate Comments\", \n", + " variant=\"primary\",\n", + " size=\"lg\"\n", + " )\n", + " \n", + " # Add some API setup information\n", + " gr.Markdown(\"## šŸ“ API Setup Instructions\")\n", + " gr.Markdown(\"\"\"\n", + " To use this tool, you need to set up API keys as environment variables:\n", + " \n", + " - **OpenAI**: Set `OPENAI_API_KEY`\n", + " - **Anthropic**: Set `ANTHROPIC_API_KEY` \n", + " - **Google Gemini**: Set `GOOGLE_API_KEY`\n", + " - **Ollama**: Make sure Ollama is running locally on port 11434\n", + " \"\"\")\n", + " \n", + " # Connect the button to the processing function\n", + " generate_btn.click(\n", + " fn=process_code,\n", + " inputs=[language_dropdown, code_input, llm_dropdown],\n", + " outputs=code_output,\n", + " show_progress=True\n", + " )\n", + " \n", + " return interface" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef461e08-c1d5-406d-b7d2-a4329f16486e", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"šŸš€ Starting Code Commenter...\")\n", + "print(\"šŸ“‹ Setting up Gradio interface...\")\n", + "\n", + "# Create and launch the interface\n", + "interface = create_gradio_interface()\n", + "\n", + "print(\"šŸŒ Launching interface...\")\n", + "print(\"šŸ’” The interface will open in your default browser\")\n", + "print(\"šŸ”§ Make sure to set up your API keys as environment variables\")\n", + "\n", + "# Launch with auto-opening in browser\n", + "interface.launch(\n", + " server_name=\"127.0.0.1\",\n", + " server_port=7860,\n", + " share=False,\n", + " inbrowser=True,\n", + " show_error=True\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}