Update app.py

app.py

@@ -128,14 +128,15 @@ class SuperSmartAgent:
         def general_reasoning_qa(state):
             question = state["question"]
         
-            # Step 1: Search Wikipedia broadly
+            # Step 1: Search Wikipedia for relevant pages
             search_results = wikipedia.search(question)
             relevant_pages = search_results[:3]  # get top 3 pages
         
             context = ""
             for title in relevant_pages:
                 try:
-                    context += wikipedia.page(title).content + "\n"
+                    page = wikipedia.page(title)
+                    context += page.content + "\n"
                 except:
                     continue
         
@@ -143,22 +144,42 @@ class SuperSmartAgent:
                 state["response"] = "Sorry, I couldn’t find enough information."
                 return state
         
-            # Step 2: Use the SuperSmartAgent to reason over the context
-            agent = SuperSmartAgent()
-            prompt = f"""
-            Based on the following Wikipedia content, answer the question:
+            # Step 2: Process the context to extract relevant information
+            # This is a simplified approach; in practice, you'd use more sophisticated NLP techniques
+            # For example, you can look for numerical data, dates, names, etc., in the context
         
-            Question: {question}
-        
-            Context:
-            {context}
+            # Example: Extract numbers and names from the context
+            import re
+            numbers = re.findall(r'\d+', context)
+            names = re.findall(r'\b[A-Z][a-z]+ [A-Z][a-z]+\b', context)  # Simplified pattern for names
+
+            # Step 3: Generate an answer based on the processed context
+            # This is a placeholder; in practice, you'd need a more sophisticated method
+            if "How many" in question:
+                if numbers:
+                    # Assume the first number is relevant (this is a very simplistic approach)
+                    state["response"] = f"The answer is {numbers[0]}."
+                else:
+                    state["response"] = "I couldn't find a numerical answer in the context."
+            elif "who" in question.lower() or "what" in question.lower():
+                if names:
+                    # Assume the first name is relevant (this is a very simplistic approach)
+                    state["response"] = f"The answer is {names[0]}."
+                else:
+                    state["response"] = "I couldn't find a relevant name in the context."
+            else:
+                # Fallback to returning a summary if no specific pattern matches
+                try:
+                    page = wikipedia.page(relevant_pages[0])
+                    summary = page.summary
+                    state["response"] = summary
+                except Exception as e:
+                    state["response"] = f"Error fetching Wikipedia content: {e}"
         
-            Answer in a single sentence.
-            """
-            state["response"] = agent(prompt)  # Use the SuperSmartAgent to process the prompt
             return state
 
 
+
         def check_reasoning_needed(state):
             q = state["question"].lower()
             # very rough heuristic — refine as needed