Update app.py

app.py

@@ -65,36 +65,43 @@ class MathAnswer(dspy.Signature):
     answer = dspy.OutputField()
 
 # === DSPy Programs ===
+import google.generativeai as genai
+
+# Configure Gemini
+genai.configure(api_key="AIzaSyBO3-HG-WcITn58PdpK7mMyvFQitoH00qA")  # Replace with your key
 
-       # return dspy.Output(answer=answer, retrieved_context=context)
 class MathRetrievalQA(dspy.Program):
     def forward(self, question):
         print("Inside MathRetrievalQA...")
         context_items = retrieve_from_qdrant(question)
         context = "\n".join([item["solution"] for item in context_items if "solution" in item])
         print("Context for generation:", context)
+
         if not context:
             return {"answer": "", "retrieved_context": ""}
 
-        # Step 1: Generate raw answer (e.g., using GPT2 or any pipeline)
-        prompt = f"Question: {question}\nContext: {context}\nAnswer:"
-        raw_answer = qa_pipeline(prompt, max_new_tokens=100)[0]["generated_text"]
-
-        # Step 2: Send raw answer to Gemini for formatting
-        format_prompt = f"""You are a helpful math assistant. Please format the following answer into a clear, step-by-step solution for better readability.
+        # Prompt Gemini to generate and format a math answer
+        prompt = f"""You are a math expert. Given the question and context below, generate a step-by-step solution in a clear, neat, and well-formatted way.
 
 Question: {question}
 
-Raw Answer:
-{raw_answer}
+Context:
+{context}
+
+Answer:"""
 
-Formatted Step-by-Step Answer:"""
+        try:
+            model = genai.GenerativeModel('gemini-2.0-flash')  # or use 'gemini-1.5-flash'
+            response = model.generate_content(prompt)
+            formatted_answer = response.text
+            print("Gemini Answer:", formatted_answer)
+            return {"answer": formatted_answer, "retrieved_context": context}
+        except Exception as e:
+            print("Gemini generation error:", e)
+            return {"answer": "⚠️ Gemini failed to generate an answer.", "retrieved_context": context}
 
-        response = gemini_model.generate_content(format_prompt)
-        formatted_answer = response.text
 
-        print("Formatted answer:", formatted_answer)
-        return {"answer": formatted_answer, "retrieved_context": context}
+       # return dspy.Output(answer=answer, retrieved_context=context)
 
 class WebFallbackQA(dspy.Program):
     def forward(self, question):