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| import os | |
| import threading | |
| import gradio as gr | |
| from openai import OpenAI | |
| from dotenv import load_dotenv | |
| # Load API keys from .env file | |
| load_dotenv() | |
| API_KEY_LLAMA = os.getenv("OPENROUTER_API_KEY1") # Llama API Key | |
| API_KEY_GEMMA = os.getenv("OPENROUTER_API_KEY2") # Gemma API Key | |
| API_KEY_DEEPSEEK1 = os.getenv("OPENROUTER_API_KEY3") # DeepSeek First Query | |
| API_KEY_DEEPSEEK2 = os.getenv("OPENROUTER_API_KEY4") # DeepSeek Final Refinement | |
| # Create OpenAI Clients for each model | |
| llama_client = OpenAI(base_url="https://openrouter.ai/api/v1", api_key=API_KEY_LLAMA) | |
| gemma_client = OpenAI(base_url="https://openrouter.ai/api/v1", api_key=API_KEY_GEMMA) | |
| deepseek_client1 = OpenAI(base_url="https://openrouter.ai/api/v1", api_key=API_KEY_DEEPSEEK1) | |
| deepseek_client2 = OpenAI(base_url="https://openrouter.ai/api/v1", api_key=API_KEY_DEEPSEEK2) | |
| # Function to query Llama model | |
| def query_llama(user_input, results): | |
| try: | |
| completion = llama_client.chat.completions.create( | |
| model="meta-llama/llama-3.2-3b-instruct:free", | |
| messages=[{"role": "user", "content": user_input}] | |
| ) | |
| results["Llama"] = completion.choices[0].message.content | |
| except Exception as e: | |
| results["Llama"] = f"Error: {str(e)}" | |
| # Function to query Gemma model | |
| def query_gemma(user_input, results): | |
| try: | |
| completion = gemma_client.chat.completions.create( | |
| model="google/gemma-2-9b-it:free", | |
| messages=[{"role": "user", "content": user_input}] | |
| ) | |
| results["Gemma"] = completion.choices[0].message.content | |
| except Exception as e: | |
| results["Gemma"] = f"Error: {str(e)}" | |
| # Function to query DeepSeek for additional context | |
| def query_deepseek_1(user_input, results): | |
| try: | |
| completion = deepseek_client1.chat.completions.create( | |
| model="deepseek/deepseek-r1:free", | |
| messages=[{"role": "user", "content": user_input}] | |
| ) | |
| results["DeepSeek1"] = completion.choices[0].message.content | |
| except Exception as e: | |
| results["DeepSeek1"] = f"Error: {str(e)}" | |
| # Function to refine responses using DeepSeek-R1 (Final API) | |
| def refine_response(user_input): | |
| try: | |
| results = {} | |
| # Create threads for parallel API calls | |
| threads = [ | |
| threading.Thread(target=query_llama, args=(user_input, results)), | |
| threading.Thread(target=query_gemma, args=(user_input, results)), | |
| threading.Thread(target=query_deepseek_1, args=(user_input, results)) | |
| ] | |
| # Start all threads | |
| for thread in threads: | |
| thread.start() | |
| # Wait for all threads to complete | |
| for thread in threads: | |
| thread.join() | |
| # Ensure all responses are received | |
| valid_responses = {k: v.strip() for k, v in results.items() if v and "Error" not in v} | |
| if len(valid_responses) < 2: | |
| return "\n\n".join(f"{k} Response: {v}" for k, v in valid_responses.items()) | |
| # Prepare refinement prompt | |
| improvement_prompt = f""" | |
| Here are three AI-generated responses: | |
| Response 1 (Llama): {results.get("Llama", "N/A")} | |
| Response 2 (Gemma): {results.get("Gemma", "N/A")} | |
| Response 3 (DeepSeek1): {results.get("DeepSeek1", "N/A")} | |
| Please combine the best elements of all three, improve clarity, and provide a final refined answer. | |
| """ | |
| # Query DeepSeek-R1 for refinement using API key 4 | |
| try: | |
| refined_completion = deepseek_client2.chat.completions.create( | |
| model="deepseek/deepseek-r1:free", | |
| messages=[{"role": "user", "content": improvement_prompt}] | |
| ) | |
| refined_content = refined_completion.choices[0].message.content | |
| return refined_content if refined_content.strip() else "Refinement failed, returning best response." | |
| except Exception as e: | |
| return f"Error refining response: {str(e)}" | |
| except Exception as e: | |
| return f"Unexpected error: {str(e)}" | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=refine_response, | |
| inputs=gr.Textbox(lines=2, placeholder="Ask me anything..."), | |
| outputs="text", | |
| title="Multi-Model AI Enhancer (4 API Keys)", | |
| description="Llama (API 1) + Gemma (API 2) + DeepSeek (API 3) → Final Refinement with DeepSeek (API 4)" | |
| ) | |
| # Launch app | |
| iface.launch() | |