Update app.py

app.py

@@ -16,35 +16,93 @@ 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()
-        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
-                        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}")
+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 # Ensure ast is imported if not already in the full file
+ 
+
+ # --- 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()
+         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 # Ensure ast is imported here if it's used in the init
+                         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}")
+ 
+
+     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.
+         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'
+ 
+
+         # If the content is an empty list, explicitly return the "unable to determine" string.
+         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.
+         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
 
 
     def __call__(self, question: str, task_id: str = None) -> str: