Update app.py

app.py

@@ -1,15 +1,9 @@
-""" Basic Agent Evaluation Runner"""
 import os
-import inspect
 import gradio as gr
 import requests
 import pandas as pd
 from langchain_core.messages import HumanMessage
 from langgraph_agent import build_graph
-from langchain_google_genai import ChatGoogleGenerativeAI
-import json
-import csv
-import ast # Added this here to ensure it's at top level
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -20,76 +14,39 @@ class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
         self.graph = build_graph()
-        self.csv_taskid_to_answer = {}
-        try:
-            with open("questions.csv", "r", encoding="utf-8") as f:
-                reader = csv.DictReader(f)
-                for row in reader:
-                    # metadata is a string like: {'task_id': 'c61d22de-5f6c-4958-a7f6-5e9707bd3466', 'level': 2}
-                    meta = row.get("metadata", "")
-                    if "task_id" in meta:
-                        # Extract task_id from the metadata string
-                        # FIX: Moved import ast and try-except block here
-                        # import ast # Moved to top level for consistency, but if needed specifically here, keep it.
-                        try:
-                            meta_dict = ast.literal_eval(meta)
-                            task_id = meta_dict.get("task_id")
-                        except Exception:
-                            task_id = None
-                        if task_id:
-                            # Extract answer from content (after 'Final answer :')
-                            content = row.get("content", "")
-                            if "Final answer :" in content:
-                                answer = content.split("Final answer :",1)[1].strip().split("\n")[0].strip()
-                                self.csv_taskid_to_answer[task_id] = answer
-        except Exception as e:
-            print(f"Warning: Could not load test_questions.csv: {e}")
 
-    # This is the correct __call__ method based on our previous discussions,
-    # and it was indented correctly relative to the class.
     def __call__(self, question: str, task_id: str = None) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
         messages = [HumanMessage(content=question)]
         messages = self.graph.invoke({"messages": messages})
 
-        # Retrieve the content of the last message
-        # If messages list is empty or the last message has no content,
-        # default to an "unable to determine" string.
+        # Check if messages or content is missing
         if not messages or not messages.get('messages') or messages['messages'][-1].content is None:
             return "I am unable to determine the information using the available tools."
 
-        answer = messages['messages'][-1].content # Keep the original variable name 'answer'
+        answer = messages['messages'][-1].content
 
-        # If the content is an empty list, explicitly return the "unable to determine" string.
+        # Handle empty list content
         if isinstance(answer, list) and not answer:
             return "I am unable to determine the information using the available tools."
-        
-        # If the content is not a string, convert it to a string.
+
+        # Convert non-string answers to string
         if not isinstance(answer, str):
-            answer = str(answer) 
-
-        # Process the answer to remove "FINAL ANSWER: " prefix if present.
-        # This moves the slicing logic to before the return statement.
-        if answer.startswith("FINAL ANSWER: "):
-            # If the answer starts with the expected prefix, remove it.
-            answer = answer[14:].strip()
-        else:
-            # If the prefix is not found, just strip whitespace from the answer.
-            # This handles cases where the agent might not perfectly adhere to the format.
-            answer = answer.strip()
-
-        # Return the processed answer, without any slicing here.
-        return answer
+            answer = str(answer)
+
+        # Normalize and strip common answer prefixes
+        answer = answer.strip()
+        prefixes = ["FINAL ANSWER:", "Final answer :", "Final answer:", "FINAL ANSWER :"]
+        for prefix in prefixes:
+            if answer.upper().startswith(prefix.upper()):
+                answer = answer[len(prefix):].strip()
+                break
 
+        # Handle empty or quote-only answers explicitly
+        if not answer or answer.strip() in ['""', "''"]:
+            answer = "I am unable to determine the information using the available tools."
 
-# This `def __call__` method was a duplicate and had incorrect indentation relative to the class.
-# It has been removed in this corrected version.
-# def __call__(self, question: str, task_id: str = None) -> str:
-#     print(f"Agent received question (first 50 chars): {question[:50]}...")
-#     messages = [HumanMessage(content=question)]
-#     messages = self.graph.invoke({"messages": messages})
-#     answer = messages['messages'][-1].content
-#     return answer[14:]
+        return answer
 
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
@@ -97,7 +54,6 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
     """
-    # Determine HF Space Runtime URL and Repo URL
     space_id = os.getenv("SPACE_ID")
 
     if profile:
@@ -154,6 +110,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             continue
         try:
             submitted_answer = agent(question_text, task_id=task_id)
+            print(f"Submitting answer for task {task_id}: '{submitted_answer}'")
             answers_payload.append({
                 "task_id": task_id,
                 "submitted_answer": submitted_answer
@@ -280,4 +237,4 @@ if __name__ == "__main__":
     print("-" * (60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)