Update app.py

app.py

@@ -5,38 +5,57 @@ import gradio as gr
 import requests
 import pandas as pd
 from langchain_core.messages import HumanMessage
-from agent import build_graph
-
+from veryfinal import build_graph
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
+# --- Basic Agent Definition ---
 class BasicAgent:
     """A langgraph agent."""
     def __init__(self):
         print("BasicAgent initialized.")
-        self.graph = build_graph()
+        try:
+            self.graph = build_graph(provider="groq")  # Using Groq as default
+            print("Graph built successfully.")
+        except Exception as e:
+            print(f"Error building graph: {e}")
+            self.graph = None
 
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        state = {
-            "messages": [HumanMessage(content=question)],
-            "query": question,
-            "agent_type": "",
-            "final_answer": "",
-            "perf": {},
-            "agno_resp": ""
-        }
+        print(f"Agent received question: {question}")
+        
+        if self.graph is None:
+            return "Error: Agent not properly initialized"
+        
+        # Wrap the question in a HumanMessage from langchain_core
+        messages = [HumanMessage(content=question)]
         config = {"configurable": {"thread_id": f"eval_{hash(question)}"}}
-        result = self.graph.invoke(state, config)
-        answer = result['messages'][-1].content
-        # Robust extraction:
-        if "FINAL ANSWER:" in answer:
-            return answer.split("FINAL ANSWER:")[-1].strip()
-        else:
-            return answer.strip()
-
-
+        
+        try:
+            result = self.graph.invoke({"messages": messages}, config)
+            
+            # Handle different response formats
+            if isinstance(result, dict):
+                if 'messages' in result and result['messages']:
+                    answer = result['messages'][-1].content
+                elif 'final_answer' in result:
+                    answer = result['final_answer']
+                else:
+                    answer = str(result)
+            else:
+                answer = str(result)
+            
+            # Extract final answer if present
+            if "FINAL ANSWER:" in answer:
+                return answer.split("FINAL ANSWER:")[-1].strip()
+            else:
+                return answer.strip()
+                
+        except Exception as e:
+            error_msg = f"Error: {str(e)}"
+            print(error_msg)
+            return error_msg
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
@@ -60,12 +79,14 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     # 1. Instantiate Agent
     try:
         agent = BasicAgent()
+        if agent.graph is None:
+            return "Error: Failed to initialize agent properly", None
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
     
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "No space ID available"
+    print(f"Agent code URL: {agent_code}")
 
     # 2. Fetch Questions
     print(f"Fetching questions from: {questions_url}")
@@ -88,19 +109,34 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+    
+    for i, item in enumerate(questions_data):
         task_id = item.get("task_id")
         question_text = item.get("question")
+        
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue
+            
+        print(f"Processing question {i+1}/{len(questions_data)}: {task_id}")
+        
         try:
             submitted_answer = agent(question_text)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+            results_log.append({
+                "Task ID": task_id, 
+                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text, 
+                "Submitted Answer": submitted_answer[:200] + "..." if len(submitted_answer) > 200 else submitted_answer
+            })
         except Exception as e:
+             error_msg = f"AGENT ERROR: {e}"
              print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+             answers_payload.append({"task_id": task_id, "submitted_answer": error_msg})
+             results_log.append({
+                 "Task ID": task_id, 
+                 "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text, 
+                 "Submitted Answer": error_msg
+             })
 
     if not answers_payload:
         print("Agent did not produce any answers to submit.")