Update model.py

Restored Groq client

core/model.py

@@ -3,12 +3,12 @@ from dotenv import load_dotenv
 from openai import OpenAI
 from groq import Groq
 import streamlit as st
-# Load environment variables from .env file
+
 load_dotenv()
 
 def generate_response(system_prompt: str, user_prompt: str, temp: float = 0.7):
     """
-    Generate a response using Gemini LLM.
+    Generate a response using Gemini or Groq LLM.
     
     Args:
         system_prompt (str): The system prompt to set the context for the model
@@ -17,30 +17,56 @@ def generate_response(system_prompt: str, user_prompt: str, temp: float = 0.7):
     Returns:
         str: The model's response
     """
-    # Get API key from environment variables
-    api_key = os.getenv("GEMINI_API_KEY")
-    
-    if not api_key:
-        raise ValueError("GEMINI_API_KEY not found in environment variables")
-    
-    # Initialize the OpenAI client with Gemini API
-    client = OpenAI(
-        api_key=api_key,
-        base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
-    )
+    try:
+        # Try Gemini first
+        api_key = os.getenv("GEMINI_API_KEY")
+        
+        if not api_key:
+            raise ValueError("GEMINI_API_KEY not found in environment variables")
+        
+        # Initialize the OpenAI client with Gemini API
+        client = OpenAI(
+            api_key=api_key,
+            base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
+        )
+        st.info("🤖 Using Gemini model")  # Debug message
+        # Create chat completion request
+        response = client.chat.completions.create(
+            model="gemini-2.0-flash-lite",
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt}
+            ],
+            temperature=temp
+        )
+        
+        # Return the generated response
+        return response.choices[0].message.content
     
-    # Create chat completion request
-    response = client.chat.completions.create(
-        model="gemini-2.0-flash-lite",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_prompt}
-        ],
-        temperature=temp
-    )
-    
-    # Return the generated response
-    return response.choices[0].message.content
+    except Exception as e:
+        # Fallback to Groq if Gemini fails
+        try:
+            groq_key = os.getenv("GROQ_API_KEY")
+            if not groq_key:
+                raise ValueError("GROQ_API_KEY not found in environment variables")
+            
+            groq_client = Groq(api_key=groq_key)
+            st.warning("⚠️ Gemini failed, using Groq model")  # Debug message
+
+            response = groq_client.chat.completions.create(
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt}
+                ],
+                model="llama-3.3-70b-versatile",
+                temperature=temp
+            )
+            
+            return response.choices[0].message.content
+            
+        except Exception as groq_error:
+            st.error(f"Both models failed. Gemini error: {e}, Groq error: {groq_error}")
+            raise
 
 # Example usage
 if __name__ == "__main__":