Update app.py

app.py

@@ -1,27 +1,25 @@
+# --- START OF FILE app.py ---
+
 import os
 import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from dotenv import load_dotenv # Added
+from agent import build_graph # Added
+from langchain_core.messages import HumanMessage # Added
+
+load_dotenv() # Added
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+# --- Basic Agent Definition --- REMOVED THIS PART
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    Fetches all questions, runs the LangGraph Agent on them, submits all answers,
     and displays the results.
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
@@ -40,15 +38,18 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        # Use the build_graph function from agent.py
+        agent_graph = build_graph() # Changed from BasicAgent()
+        print("LangGraph agent initialized.")
     except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
+        print(f"Error instantiating agent graph: {e}")
+        return f"Error initializing agent graph: {e}", None
     # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
     # 2. Fetch Questions
+    # ... (rest of fetching code is the same) ...
     print(f"Fetching questions from: {questions_url}")
     try:
         response = requests.get(questions_url, timeout=15)
@@ -69,6 +70,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
+
     # 3. Run your Agent
     results_log = []
     answers_payload = []
@@ -80,23 +82,42 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)
+            # Invoke the LangGraph agent
+            result_state = agent_graph.invoke({"messages": [HumanMessage(content=question_text)]})
+
+            # Extract the final answer from the last message
+            submitted_answer = "Error: Agent did not provide a response." # Default in case extraction fails
+            if result_state and "messages" in result_state and result_state["messages"]:
+                last_message = result_state["messages"][-1]
+                 # The final content is typically in the content attribute of the last message
+                if hasattr(last_message, 'content') and last_message.content:
+                    submitted_answer = last_message.content
+                # else: Handle cases where the last message might be a tool message etc.,
+                # for simplicity, we just use the default error message if content is missing.
+
+             # Ensure submitted_answer is a string
+            if not isinstance(submitted_answer, str):
+                 submitted_answer = str(submitted_answer)
+
             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})
         except Exception as e:
-             print(f"Error running agent on task {task_id}: {e}")
+             print(f"Error running agent graph on task {task_id}: {e}")
              results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
 
     if not answers_payload:
         print("Agent did not produce any answers to submit.")
+        # Even if no answers, show the log of errors
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 4. Prepare Submission 
+
+    # 4. Prepare Submission
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
 
     # 5. Submit
+    # ... (rest of submission code is the same) ...
     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
@@ -142,14 +163,15 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# LangGraph Agent Evaluation Runner") # Updated title
     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.
+        1.  Please clone this space, then modify the code in `agent.py` and `app.py` to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  **Make sure you have your `DEEPSEEK_API_KEY` set as a Space Secret.**
+        3.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        4.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
 
         ---
         **Disclaimers:**
@@ -192,6 +214,6 @@ if __name__ == "__main__":
 
     print("-"*(60 + len(" App Starting ")) + "\n")
 
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
-    
+    print("Launching Gradio Interface for LangGraph Agent Evaluation...") # Updated message
+    # demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False, auth=None) # Keep auth=None for public space or remove for gated