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Create app.py
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app.py
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import os
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import gradio as gr
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from huggingface_hub import InferenceClient
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from openai import OpenAI
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from dotenv import load_dotenv
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# Load API keys from .env file
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load_dotenv()
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HF_API_KEY = os.getenv("HF_API_KEY")
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OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
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# Initialize Hugging Face Gemma Client
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hf_client = InferenceClient(
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provider="hf-inference",
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api_key=HF_API_KEY
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)
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# Initialize OpenRouter DeepSeek Client
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openrouter_client = OpenAI(
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base_url="https://openrouter.ai/api/v1",
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api_key=OPENROUTER_API_KEY
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)
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# Function to query Gemma
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def query_gemma(user_input):
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messages = [{"role": "user", "content": user_input}]
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completion = hf_client.chat.completions.create(
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model="google/gemma-2-27b-it",
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messages=messages,
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max_tokens=500
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)
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return completion.choices[0].message["content"]
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# Function to query DeepSeek
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def query_deepseek(user_input):
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completion = openrouter_client.chat.completions.create(
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model="deepseek/deepseek-r1:free",
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messages=[{"role": "user", "content": user_input}]
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)
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return completion.choices[0].message.content
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# Function to refine response using DeepSeek
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def refine_response(user_input):
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# Get responses from both models
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gemma_response = query_gemma(user_input)
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deepseek_response = query_deepseek(user_input)
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# Send both responses to DeepSeek for refinement
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improvement_prompt = f"""
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Here are two AI-generated responses:
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Response 1 (Gemma): {gemma_response}
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Response 2 (DeepSeek): {deepseek_response}
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Please combine the best elements, improve clarity, and provide a final refined answer.
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"""
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refined_completion = openrouter_client.chat.completions.create(
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model="deepseek/deepseek-r1:free",
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messages=[{"role": "user", "content": improvement_prompt}]
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)
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return refined_completion.choices[0].message.content
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# Create Gradio interface
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iface = gr.Interface(
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fn=refine_response,
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inputs=gr.Textbox(lines=2, placeholder="Ask me anything..."),
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outputs="text",
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title="AI Response Enhancer",
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description="Get responses from both Gemma and DeepSeek, then receive an improved final answer."
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)
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# Launch app
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iface.launch()
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