Update app.py

app.py

@@ -129,7 +129,7 @@ class BasicAgent:
     def _call_llm(self, prompt: str, max_tokens: int = 256) -> str:
         try:
             resp = self.llm.chat.completions.create(
-                model="gpt-4.1-mini",
+                model="gpt-4o-mini",
                 messages=[
                     {"role": "system", "content": "You are a careful reasoning assistant."},
                     {"role": "user", "content": prompt},
@@ -154,18 +154,9 @@ class BasicAgent:
                 state["current_step"] = "sheet"
             return state
 
-        # Check if question is about the file we just processed
-        if state["history"]:
-            last_step = state["history"][-1]["step"]
-            if last_step in ["video", "image", "sheet"]:
-                state["current_step"] = "answer"
-                return state
-
         # Regular text question analysis
         prompt = (
-            "Decide if this question needs web search. Only use search for factual questions that require external knowledge. "
-            "For questions about files we just processed or simple reasoning, don't use search. "
-            "Respond with a Python dict:\n"
+            "Decide if this question needs web search. Respond with a Python dict:\n"
             "{\n  'needs_search': bool,\n  'search_query': str\n}\n\n"
             f"Question: {state['question']}"
         )
@@ -176,7 +167,7 @@ class BasicAgent:
             state["search_query"] = decision.get("search_query", state["question"])
         except Exception as e:
             print(f"\nLLM Error in question analysis: {str(e)}")
-            state["needs_search"] = False  # Default to no search on error
+            state["needs_search"] = True
             state["search_query"] = state["question"]
             
         state["current_step"] = "search" if state["needs_search"] else "answer"
@@ -286,8 +277,10 @@ class BasicAgent:
         # Join all materials with clear separation
         search_block = "\n\n".join(materials) if materials else "No materials available."
         
+        # First attempt with full context
         prompt = f"""
-Answer this question using ONLY the materials provided.
+You are a helpful assistant. Your task is to answer the question using ONLY the materials provided.
+If you cannot find a direct answer, provide the most relevant information you can find.
 
 QUESTION:
 {state['question']}
@@ -301,10 +294,40 @@ Write ANSWER: <answer> on its own line.
             raw = self._call_llm(prompt, 300)
             answer = raw.split("ANSWER:")[-1].strip()
             
-            if not answer:
-                answer = "I cannot provide a definitive answer at this time."
-            elif "ANSWER:" not in raw:
-                answer = "I cannot provide a definitive answer at this time."
+            # If first attempt fails or is empty, try a more direct prompt
+            if not answer or "ANSWER:" not in raw or any(k in answer.lower() for k in ["cannot", "sorry", "don't know"]):
+                print("\nFirst attempt failed, trying direct prompt...")
+                direct_prompt = f"""
+Answer this question directly and concisely. Use the materials provided.
+
+QUESTION:
+{state['question']}
+
+MATERIALS:
+{search_block}
+
+If you cannot find an exact answer, provide the most relevant information from the materials.
+Write ANSWER: <answer> on its own line.
+"""
+                raw = self._call_llm(direct_prompt, 300)
+                answer = raw.split("ANSWER:")[-1].strip()
+            
+            # Final validation and fallback
+            if not answer or "ANSWER:" not in raw:
+                print("\nBoth attempts failed, using fallback answer...")
+                if materials:
+                    # If we have materials but no answer, summarize what we know
+                    summary_prompt = f"""
+Summarize the key information from these materials in one sentence:
+
+{search_block}
+
+Write ANSWER: <answer> on its own line.
+"""
+                    raw = self._call_llm(summary_prompt, 150)
+                    answer = raw.split("ANSWER:")[-1].strip()
+                else:
+                    answer = "I cannot provide a definitive answer at this time."
             
             state["final_answer"] = answer
             state["current_step"] = "done"