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| import os | |
| import time | |
| import json | |
| import httpx | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from dotenv import load_dotenv | |
| # Load API keys from .env file | |
| load_dotenv() | |
| HF_API_KEY = os.getenv("HF_API_KEY") # Hugging Face API for Gemma & Llama | |
| TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY") # Together AI API for DeepSeek-V3 | |
| # Initialize Hugging Face Clients | |
| hf_client = InferenceClient(provider="hf-inference", api_key=HF_API_KEY) | |
| together_client = InferenceClient(provider="together", api_key=TOGETHER_API_KEY) | |
| # Query Hugging Face Models (Gemma & Llama) | |
| def query_huggingface_model(user_input, model_name): | |
| try: | |
| messages = [{"role": "user", "content": user_input}] | |
| completion = hf_client.chat.completions.create( | |
| model=model_name, | |
| messages=messages, | |
| max_tokens=500 | |
| ) | |
| return completion.choices[0].message["content"] | |
| except Exception as e: | |
| return f"Error querying {model_name}: {str(e)}" | |
| # Query DeepSeek-V3 (Together AI via Hugging Face) | |
| def query_deepseek_v3(user_input): | |
| try: | |
| messages = [{"role": "user", "content": user_input}] | |
| completion = together_client.chat.completions.create( | |
| model="deepseek-ai/DeepSeek-V3", | |
| messages=messages, | |
| max_tokens=500 | |
| ) | |
| return completion.choices[0].message["content"] | |
| except Exception as e: | |
| return f"Error querying DeepSeek-V3: {str(e)}" | |
| # Function to refine responses using DeepSeek-V3 | |
| def refine_response(user_input): | |
| try: | |
| # Get responses from all three models | |
| gemma_response = query_huggingface_model(user_input, "google/gemma-2-27b-it") | |
| llama_response = query_huggingface_model(user_input, "meta-llama/Llama-3.3-70B-Instruct") | |
| deepseek_response = query_deepseek_v3(user_input) | |
| # If any response is missing, return the available ones | |
| responses = { | |
| "Gemma": gemma_response.strip(), | |
| "Llama": llama_response.strip(), | |
| "DeepSeek-V3": deepseek_response.strip() | |
| } | |
| valid_responses = {k: v for k, v in responses.items() if 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 (Gemma): {gemma_response} | |
| Response 2 (Llama 3.3): {llama_response} | |
| Response 3 (DeepSeek-V3): {deepseek_response} | |
| Please combine the best elements of all three, improve clarity, and provide a final refined answer. | |
| """ | |
| # Retry loop for DeepSeek-V3 refinement | |
| max_retries = 3 | |
| for attempt in range(max_retries): | |
| try: | |
| messages = [{"role": "user", "content": improvement_prompt}] | |
| refined_completion = together_client.chat.completions.create( | |
| model="deepseek-ai/DeepSeek-V3", | |
| messages=messages, | |
| max_tokens=500 | |
| ) | |
| refined_content = refined_completion.choices[0].message["content"] | |
| if refined_content.strip(): | |
| return refined_content | |
| else: | |
| print("Received empty response from DeepSeek-V3, retrying...") | |
| time.sleep(2) | |
| except Exception as e: | |
| print(f"Error on attempt {attempt + 1}: {str(e)}") | |
| time.sleep(2) | |
| return f"Refinement failed. Here’s the best available response:\n\n{max(valid_responses.values(), key=len)}" | |
| except Exception as e: | |
| return f"Error refining response: {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", | |
| description="Get responses from Gemma, Llama 3.3, and DeepSeek-V3. Then receive an improved final answer." | |
| ) | |
| # Launch app | |
| iface.launch(debug=True) | |