Update app.py

app.py

@@ -1,4 +1,6 @@
 import os
+import json
+import httpx
 import gradio as gr
 from huggingface_hub import InferenceClient
 from openai import OpenAI
@@ -21,46 +23,76 @@ openrouter_client = OpenAI(
     api_key=OPENROUTER_API_KEY
 )
 
-# Function to query Gemma
+# Function to query Gemma-2-27B (Hugging Face)
 def query_gemma(user_input):
-    messages = [{"role": "user", "content": user_input}]
-    completion = hf_client.chat.completions.create(
-        model="google/gemma-2-27b-it", 
-        messages=messages, 
-        max_tokens=500
-    )
-    return completion.choices[0].message["content"]
+    try:
+        messages = [{"role": "user", "content": user_input}]
+        completion = hf_client.chat.completions.create(
+            model="google/gemma-2-27b-it", 
+            messages=messages, 
+            max_tokens=500
+        )
+        return completion.choices[0].message["content"]
+    except Exception as e:
+        return f"Error querying Gemma: {str(e)}"
 
-# Function to query DeepSeek
+# Function to query DeepSeek-R1 (OpenRouter)
 def query_deepseek(user_input):
-    completion = openrouter_client.chat.completions.create(
-        model="deepseek/deepseek-r1:free",
-        messages=[{"role": "user", "content": user_input}]
-    )
-    return completion.choices[0].message.content
+    try:
+        completion = openrouter_client.chat.completions.create(
+            model="deepseek/deepseek-r1:free",
+            messages=[{"role": "user", "content": user_input}]
+        )
+        return completion.choices[0].message.content
+    except Exception as e:
+        return f"Error querying DeepSeek: {str(e)}"
 
-# Function to refine response using DeepSeek
+# Function to refine responses using DeepSeek
 def refine_response(user_input):
-    # Get responses from both models
-    gemma_response = query_gemma(user_input)
-    deepseek_response = query_deepseek(user_input)
+    try:
+        # Get responses from both models
+        gemma_response = query_gemma(user_input)
+        deepseek_response = query_deepseek(user_input)
 
-    # Send both responses to DeepSeek for refinement
-    improvement_prompt = f"""
-    Here are two AI-generated responses:
+        # If either response failed, return the available one
+        if "Error" in gemma_response:
+            return f"Only DeepSeek Response:\n{deepseek_response}"
+        if "Error" in deepseek_response:
+            return f"Only Gemma Response:\n{gemma_response}"
 
-    Response 1 (Gemma): {gemma_response}
-    Response 2 (DeepSeek): {deepseek_response}
+        # Prepare refinement prompt
+        improvement_prompt = f"""
+        Here are two AI-generated responses:
 
-    Please combine the best elements, improve clarity, and provide a final refined answer.
-    """
-    
-    refined_completion = openrouter_client.chat.completions.create(
-        model="deepseek/deepseek-r1:free",
-        messages=[{"role": "user", "content": improvement_prompt}]
-    )
-    
-    return refined_completion.choices[0].message.content
+        Response 1 (Gemma): {gemma_response}
+        Response 2 (DeepSeek): {deepseek_response}
+
+        Please combine the best elements of both, improve clarity, and provide a final refined answer.
+        """
+        
+        # Send request to OpenRouter
+        response = httpx.post(
+            "https://openrouter.ai/api/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {OPENROUTER_API_KEY}",
+                "Content-Type": "application/json"
+            },
+            json={
+                "model": "deepseek/deepseek-r1:free",
+                "messages": [{"role": "user", "content": improvement_prompt}]
+            }
+        )
+
+        # Print raw response for debugging
+        print("OpenRouter Response:", response.text)
+
+        # Check if response is valid JSON
+        response_json = response.json()
+        
+        return response_json["choices"][0]["message"]["content"]
+
+    except Exception as e:
+        return f"Error refining response: {str(e)}"
 
 # Create Gradio interface
 iface = gr.Interface(