Update app.py

app.py

@@ -27,10 +27,12 @@ except ImportError:
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
+# ----- THIS IS WHERE THE ADVANCED AGENT LOGIC IS BUILT -----
 class BasicAgent:
     def __init__(self):
         print("Initializing Advanced Agent logic...")
         try:
+            # Using hardcoded values for endpoint/deployment and loading the key from secrets
             self.llm = AzureChatOpenAI(
                 azure_endpoint="https://dsap.openai.azure.com/",
                 api_key=os.environ["AZURE_API_KEY"],
@@ -41,10 +43,11 @@ class BasicAgent:
             )
             print("AzureChatOpenAI client initialized successfully.")
         except KeyError:
-            raise KeyError("CRITICAL: Missing 'AZURE_API_KEY'. Please add it to your Space secrets.")
+            raise KeyError("CRITICAL: Missing 'AZURE_API_KEY' secret. Please add it to your Space settings.")
         except Exception as e:
             print(f"FAILED to initialize Azure client. Error: {e}")
             raise
+
         self.tools = {
             "search": self.search, "browse": self.browse,
             "python": self.python_interpreter, "get_youtube_transcript": self.get_youtube_transcript,
@@ -145,47 +148,58 @@ Thought:
                 return llm_response
         return "Agent failed to find an answer within the loop limit."
 
-
-# The function signature is changed to accept the profile object directly
-def run_and_submit_all(profile: gr.OAuthProfile | None):
+# Using your original submission function structure
+def run_and_submit_all( profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
 
-    if profile and profile.username:
-        username = profile.username
+    if profile:
+        username= f"{profile.username}"
         print(f"User logged in: {username}")
     else:
-        # This is the message you were seeing.
-        return "Could not get user profile. Please log in using the button above.", None
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
 
-    api_url, submit_url = DEFAULT_API_URL, f"{DEFAULT_API_URL}/submit"
+    api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
 
-    try: agent = BasicAgent()
-    except Exception as e: return f"Error initializing agent: {e}", None
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        return f"Error initializing agent: {e}", None
 
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "https://github.com/huggingface/agents-course"
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else ""
+    print(f"Agent code URL: {agent_code}")
 
     try:
         response = requests.get(questions_url, timeout=20)
         response.raise_for_status()
         questions_data = response.json()
-        if not questions_data: return "Fetched questions list is empty.", None
+        if not questions_data:
+             return "Fetched questions list is empty.", None
         print(f"Fetched {len(questions_data)} questions.")
-    except Exception as e: return f"Error fetching questions: {e}", None
+    except Exception as e:
+        return f"Error fetching questions: {e}", None
 
     results_log, answers_payload = [], []
+    print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
-        task_id, question_text = item.get("task_id"), item.get("question")
-        if not task_id or question_text is None: continue
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or question_text is None:
+            continue
         try:
+            # *** The only required change to your original function ***
+            # We pass the whole 'item' so the agent can see file URLs.
             submitted_answer = agent(item)
             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:
-            error_msg = f"AGENT ERROR: {traceback.format_exc()}"
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": error_msg})
+        except Exception as e:
+             error_msg = f"AGENT ERROR: {traceback.format_exc()}"
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": error_msg})
 
-    if not answers_payload: return "Agent did not produce answers.", pd.DataFrame(results_log)
+    if not answers_payload:
+        return "Agent did not produce any answers.", pd.DataFrame(results_log)
 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     print(f"Submitting {len(answers_payload)} answers for user '{username}'...")
@@ -193,40 +207,43 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
         response.raise_for_status()
-        result_data = response.json()
-        final_status = (f"Submission Successful!\nUser: {result_data.get('username')}\n"
-                        f"Overall Score: {result_data.get('score', 'N/A')}% "
-                        f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)")
+        result__data = response.json()
+        final_status = (
+            f"Submission Successful!\nUser: {result_data.get('username')}\n"
+            f"Overall Score: {result_data.get('score', 'N/A')}% "
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)"
+        )
         return final_status, pd.DataFrame(results_log)
-    except Exception as e: return f"Submission Failed: {e}", pd.DataFrame(results_log)
-
-
-# --- Build Gradio Interface ---
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# Agent Evaluation Runner")
-
-    # This state object will hold the user profile. It's invisible.
-    profile_state = gr.State(value=None)
-    
-    with gr.Row():
-        # The login button is now just for show and triggering the login flow.
-        gr.LoginButton()
-
-    with gr.Row():
-        run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
-
-    with gr.Column():
-        status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-        results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    # *** THE NEW, ROBUST FIX IS HERE ***
-    # 1. When the page loads, get the user's auth info and store it in our invisible state object.
-    demo.load(lambda request: request.auth, inputs=[], outputs=[profile_state])
+    except Exception as e:
+        return f"Submission Failed: {e}", pd.DataFrame(results_log)
+
+
+# --- Build Gradio Interface using Blocks ---
+# Using the corrected wiring for the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    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).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution.
+        """
+    )
+    # The fix is to give the login button a variable name...
+    login_button = 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)
 
-    # 2. When the run button is clicked, take the stored profile from the state object as input.
+    # ...and pass it as an input to the click event. This is the standard, working method.
     run_button.click(
         fn=run_and_submit_all,
-        inputs=[profile_state],
+        inputs=[login_button],
         outputs=[status_output, results_table]
     )