final update

app.py

@@ -3,79 +3,28 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from langchain_core.messages import HumanMessage
+from agent import build_graph
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-
-class WikipediaSearchTool:
-    def search(self, query: str) -> str:
-        # 假裝我們真的去Wikipedia查到了
-        if "Mercedes Sosa" in query:
-            return """Between 2000 and 2009, Mercedes Sosa released the following studio albums:
-            - Corazón Libre (2005)
-            - Cantora 1 (2009)
-            - Cantora 2 (2009)
-            """
-        return "No information found."
-
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        self.wikipedia_tool = WikipediaSearchTool()
         print("BasicAgent initialized.")
+        self.graph = build_graph()
 
     def __call__(self, question: str) -> str:
-        print(f"Agent received question: {question}")
-
-        if "studio albums" in question and "Mercedes Sosa" in question:
-            wiki_text = self.wikipedia_tool.search("Mercedes Sosa studio albums between 2000 and 2009")
-            album_list = self.extract_albums(wiki_text)
-            album_count = len(album_list)
-            return str(album_count)
-        elif "L1vXCYZAYYM" in question:
-            return str(3)
-        elif "tfel" in question:
-            return str("right")
-        elif "Featured Article" in question and "November 2016" in question:
-            return str("FunkMonk")
-        elif "table defining" in question:
-            return str("b,e")
-        elif "1htKBjuUWec" in question:
-            return str("Extremely")
-        elif "CK-12 license" in question:
-            return str("Louvrier")
-        elif "grocery list" in question:
-            return str("broccoli, celery, fresh basil, lettuce, sweet potatoes")
-        elif "CK-12 license" in question:
-            return str("Louvrier")
-        elif "Everybody Loves Raymond" in question:
-            return str("Wojciech")
-        elif "Homework.mp3" in question:
-            return str("132, 133, 134, 197, 245")
-        elif "fast-food chain" in question:
-            return str(89706.00)
-        elif "Yankee " in question:
-            return str(519)
-        elif "Carolyn Collins Petersen" in question:
-            return str("80GSFC21M0002")
-        elif "Vietnamese specimens" in question:
-            return str("Saint Petersburg")
-        elif "Olympics" in question:
-            return str("CUB")
-        elif "pitchers" in question and "Taishō Tamai" in question:
-            return str("Yoshida, Uehara")
-        elif "Malko Competition" in question:
-            return str("Dmitry")
-        else:
-            return "This is a default answer."
-
-    def extract_albums(self, wiki_text: str) -> list:
-        lines = wiki_text.split("\n")
-        albums = [line.strip() for line in lines if "-" in line]
-        return albums
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        # Wrap the question in a HumanMessage from langchain_core
+        messages = [HumanMessage(content=question)]
+        messages = self.graph.invoke({"messages": messages})
+        answer = messages['messages'][-1].content
+        return answer[14:]
+
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -204,9 +153,11 @@ with gr.Blocks() as demo:
     gr.Markdown(
         """
         **Instructions:**
+
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+
         ---
         **Disclaimers:**
         Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).