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| import os | |
| import logging | |
| import httpx | |
| from dotenv import load_dotenv | |
| import gradio as gr | |
| from typing import AsyncGenerator, List, Dict, Tuple | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Load environment variables | |
| load_dotenv() | |
| logger.info("Environment variables loaded from .env file") | |
| logger.info(f"OPENAI_API_KEY present: {'OPENAI_API_KEY' in os.environ}") | |
| logger.info(f"ANTHROPIC_API_KEY present: {'ANTHROPIC_API_KEY' in os.environ}") | |
| logger.info(f"GEMINI_API_KEY present: {'GEMINI_API_KEY' in os.environ}") | |
| async def ask_openai(query: str, history: List[Dict[str, str]]) -> AsyncGenerator[str, None]: | |
| openai_api_key = os.getenv("OPENAI_API_KEY") | |
| if not openai_api_key: | |
| logger.error("OpenAI API key not provided") | |
| yield "Error: OpenAI API key not provided." | |
| return | |
| # Build message history | |
| messages = [] | |
| for msg in history: | |
| messages.append({"role": "user", "content": msg["user"]}) | |
| if msg["bot"]: | |
| messages.append({"role": "assistant", "content": msg["bot"]}) | |
| messages.append({"role": "user", "content": query}) | |
| headers = { | |
| "Authorization": f"Bearer {openai_api_key}", | |
| "Content-Type": "application/json" | |
| } | |
| payload = { | |
| "model": "gpt-3.5-turbo", | |
| "messages": messages, | |
| "stream": True | |
| } | |
| try: | |
| async with httpx.AsyncClient() as client: | |
| async with client.stream("POST", "https://api.openai.com/v1/chat/completions", headers=headers, json=payload) as response: | |
| response.raise_for_status() | |
| async for chunk in response.intro_text(): | |
| if chunk: | |
| # Parse the streaming chunk (JSON lines) | |
| lines = chunk.splitlines() | |
| for line in lines: | |
| if line.startswith("data: "): | |
| data = line[6:] # Remove "data: " prefix | |
| if data == "[DONE]": | |
| break | |
| try: | |
| json_data = eval(data) # Safely parse JSON | |
| if "choices" in json_data and json_data["choices"]: | |
| delta = json_data["choices"][0].get("delta", {}) | |
| if "content" in delta: | |
| yield delta["content"] | |
| except Exception as e: | |
| logger.error(f"Error parsing OpenAI stream chunk: {str(e)}") | |
| yield f"Error parsing stream: {str(e)}" | |
| except httpx.HTTPStatusError as e: | |
| logger.error(f"OpenAI HTTP Status Error: {e.response.status_code}, {e.response.text}") | |
| yield f"Error: OpenAI HTTP Status Error: {e.response.status_code}, {e.response.text}" | |
| except Exception as e: | |
| logger.error(f"OpenAI Error: {str(e)}") | |
| yield f"Error: OpenAI Error: {str(e)}" | |
| async def ask_anthropic(query: str, history: List[Dict[str, str]]) -> str: | |
| anthropic_api_key = os.getenv("ANTHROPIC_API_KEY") | |
| if not anthropic_api_key: | |
| logger.error("Anthropic API key not provided") | |
| return "Error: Anthropic API key not provided." | |
| # Build message history | |
| messages = [] | |
| for msg in history: | |
| messages.append({"role": "user", "content": msg["user"]}) | |
| if msg["bot"]: | |
| messages.append({"role": "assistant", "content": msg["bot"]}) | |
| messages.append({"role": "user", "content": query}) | |
| headers = { | |
| "x-api-key": anthropic_api_key, | |
| "anthropic-version": "2023-06-01", | |
| "Content-Type": "application/json" | |
| } | |
| payload = { | |
| "model": "claude-3-5-sonnet-20241022", | |
| "max_tokens": 1024, | |
| "messages": messages | |
| } | |
| try: | |
| async with httpx.AsyncClient() as client: | |
| logger.info(f"Sending Anthropic request: {payload}") | |
| response = await client.post("https://api.anthropic.com/v1/messages", headers=headers, json=payload) | |
| response.raise_for_status() | |
| logger.info(f"Anthropic response: {response.json()}") | |
| return response.json()['content'][0]['text'] | |
| except httpx.HTTPStatusError as e: | |
| logger.error(f"Anthropic HTTP Status Error: {e.response.status_code}, {e.response.text}") | |
| return f"Error: Anthropic HTTP Status Error: {e.response.status_code}, {e.response.text}" | |
| except Exception as e: | |
| logger.error(f"Anthropic Error: {str(e)}") | |
| return f"Error: Anthropic Error: {str(e)}" | |
| async def ask_gemini(query: str, history: List[Dict[str, str]]) -> str: | |
| gemini_api_key = os.getenv("GEMINI_API_KEY") | |
| if not gemini_api_key: | |
| logger.error("Gemini API key not provided") | |
| return "Error: Gemini API key not provided." | |
| # Gemini doesn't natively support chat history in the same way, so we concatenate history as text | |
| history_text = "" | |
| for msg in history: | |
| history_text += f"User: {msg['user']}\nAssistant: {msg['bot']}\n" if msg["bot"] else f"User: {msg['user']}\n" | |
| full_query = history_text + f"User: {query}\n" | |
| headers = { | |
| "Content-Type": "application/json" | |
| } | |
| payload = { | |
| "contents": [{"parts": [{"text": full_query}]}] | |
| } | |
| try: | |
| async with httpx.AsyncClient() as client: | |
| response = await client.post( | |
| f"https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key={gemini_api_key}", | |
| headers=headers, | |
| json=payload | |
| ) | |
| response.raise_for_status() | |
| return response.json()['candidates'][0]['content']['parts'][0]['text'] | |
| except httpx.HTTPStatusError as e: | |
| logger.error(f"Gemini HTTP Status Error: {e.response.status_code}, {e.response.text}") | |
| return f"Error: Gemini HTTP Status Error: {e.response.status_code}, {e.response.text}" | |
| except Exception as e: | |
| logger.error(f"Gemini Error: {str(e)}") | |
| return f"Error: Gemini Error: {str(e)}" | |
| async def query_model(query: str, provider: str, history: List[Dict[str, str]]) -> AsyncGenerator[Tuple[str, List[Dict[str, str]]], None]: | |
| provider = provider.lower() | |
| response = "" | |
| if provider == "openai": | |
| async for chunk in ask_openai(query, history): | |
| response += chunk | |
| yield chunk, history # Yield partial response for streaming | |
| elif provider == "anthropic": | |
| response = await ask_anthropic(query, history) | |
| yield response, history | |
| elif provider == "gemini": | |
| response = await ask_gemini(query, history) | |
| yield response, history | |
| else: | |
| response = f"Error: Unknown provider: {provider}" | |
| yield response, history | |
| # Update history with the new query and response | |
| updated_history = history + [{"user": query, "bot": response}] | |
| logger.info(f"Updated history: {updated_history}") | |
| yield response, updated_history # Final yield with updated history | |
| async def submit_query(query: str, provider: str, history: List[Dict[str, str]]) -> AsyncGenerator[Tuple[str, List[Dict[str, str]], List[Dict[str, str]]], None]: | |
| if not query.strip(): | |
| yield "", history, history | |
| return | |
| response = "" | |
| async for response_chunk, updated_history in query_model(query, provider, history): | |
| response += response_chunk | |
| yield "", updated_history, updated_history # Yield intermediate updates for streaming | |
| yield "", updated_history, updated_history # Final yield with cleared query | |
| # Gradio interface | |
| def clear_history(): | |
| return [], [] | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("# Multi-Model Chat") | |
| gr.Markdown("Chat with OpenAI, Anthropic, or Gemini. Select a provider and start typing!") | |
| provider = gr.Dropdown(choices=["OpenAI", "Anthropic", "Gemini"], label="Select Provider", value="OpenAI") | |
| history_state = gr.State(value=[]) | |
| chatbot = gr.Chatbot(label="Conversation", type="messages") | |
| query = gr.Textbox(label="Enter your query", placeholder="e.g., What is the capital of the United States?") | |
| submit_button = gr.Button("Submit") | |
| clear_button = gr.Button("Clear History") | |
| submit_button.click( | |
| fn=submit_query, | |
| inputs=[query, provider, history_state], | |
| outputs=[query, chatbot, history_state] | |
| ) | |
| clear_button.click( | |
| fn=clear_history, | |
| inputs=[], | |
| outputs=[chatbot, history_state] | |
| ) | |
| # Launch the Gradio app | |
| demo.launch() |