Update app.py

app.py

@@ -1,26 +1,27 @@
-""" Basic Agent Evaluation Runner"""
+""" Basic Agent Evaluation Runner """
 import os
 import gradio as gr
 import requests
 import pandas as pd
 from langchain_core.messages import HumanMessage
-from agent import ninu  # استيراد دالة بناء الـ agent من ملف agent.py
+from agent import ninu  # استيراد الوكيل من agent.py
 
-# Constants
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 class BasicAgent:
-    """A langgraph agent."""
+    """Agent class using LangGraph compiled graph."""
     def __init__(self):
         print("BasicAgent initialized.")
-        self.graph = ninu()  # نستخدم دالة ninu لبناء الـ graph
+        self.graph = ninu  # ما تنساش: ما تعملش () هنا لأن `ninu` هو كائن compiled
 
     def __call__(self, question: str) -> 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:]  # تعديل حسب البنية التي يرجعها النموذج
+        result = self.graph.invoke({"messages": messages})
+        for message in reversed(result["messages"]):
+            if isinstance(message.content, str) and "FINAL ANSWER:" in message.content:
+                return message.content.split("FINAL ANSWER:")[-1].strip()
+        return "لم أتمكن من إيجاد إجابة نهائية."
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
@@ -97,7 +98,6 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     except Exception as e:
         return f"Submission Failed: {e}", pd.DataFrame(results_log)
 
-
 # Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")
@@ -111,9 +111,7 @@ with gr.Blocks() as demo:
     )
 
     gr.LoginButton()
-
     run_button = gr.Button("Run Evaluation & Submit All Answers")
-
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)