Update app.py

app.py

@@ -1,89 +1,126 @@
 # 17/08/2025 -AO
+
 import os
 import gradio as gr
 import requests
 import pandas as pd
-
-from agent import BasicAgent
+from transformers import HfAgent
+from transformers.tools import DuckDuckGoSearchTool
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
+# --- Advanced Agent Definition ---
+class MyAgent:
+    def __init__(self):
+        # Define the LLM for the agent. Use a supported model from the Hugging Face Hub.
+        # Ensure you have the necessary environment variables (e.g., HF_TOKEN).
+        # You'll likely need a powerful model to get a high score.
+        # Example: Using a powerful hosted model
+        self.agent = HfAgent("HuggingFaceH4/zephyr-7b-beta",
+                             additional_tools=[DuckDuckGoSearchTool()])
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question: {question[:50]}...")
+        # Use a try-except block to handle potential errors during execution.
+        try:
+            # Let the agent reason and find the answer.
+            # Use verbose=True to see the agent's thought process for debugging.
+            result = self.agent.run(question)
+            # The GAIA scoring is an "EXACT MATCH".
+            # The prompt asks the agent to just return the answer, not a verbose explanation.
+            # You might need to add a post-processing step to extract the final answer.
+            # For example, by telling the model in the prompt to only output the answer.
+            final_answer = str(result)
+            print(f"Agent returning answer: {final_answer}")
+            return final_answer
+        except Exception as e:
+            print(f"An error occurred during agent execution: {e}")
+            return f"Error: {e}"
 
+# The rest of the `run_and_submit_all` and Gradio code remains the same as in the template.
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    Fetch all questions, run the BasicAgent on them, submit all answers, and display results.
+    Fetches all questions, runs the agent on them, submits all answers,
+    and displays the results.
     """
     space_id = os.getenv("SPACE_ID")
 
     if profile:
         username = f"{profile.username}"
+        print(f"User logged in: {username}")
     else:
+        print("User not logged in.")
         return "Please Login to Hugging Face with the button.", None
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1) Agent
+    # 1. Instantiate Agent
     try:
-        agent = BasicAgent(api_url=api_url)
+        agent = MyAgent()  # Instantiate your new agent class
     except Exception as e:
+        print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
+    
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(agent_code)
 
-    agent_code = (
-        f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "N/A"
-    )
-
-    # 2) Fetch Questions
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
     try:
-        resp = requests.get(questions_url, timeout=15)
-        resp.raise_for_status()
-        questions_data = resp.json()
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
         if not questions_data:
-            return "Fetched questions list is empty or invalid format.", None
+             print("Fetched questions list is empty.")
+             return "Fetched questions list is empty or invalid format.", None
+        print(f"Fetched {len(questions_data)} questions.")
     except requests.exceptions.RequestException as e:
+        print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
+    except requests.exceptions.JSONDecodeError as e:
+         print(f"Error decoding JSON response from questions endpoint: {e}")
+         print(f"Response text: {response.text[:500]}")
+         return f"Error decoding server response for questions: {e}", None
     except Exception as e:
+        print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
-    # 3) Run Agent
+    # 3. Run your Agent
     results_log = []
     answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
             submitted_answer = agent(question_text)
             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,
-            })
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e:
-            results_log.append({
-                "Task ID": task_id,
-                "Question": question_text,
-                "Submitted Answer": f"AGENT ERROR: {e}",
-            })
+             print(f"Error running agent 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.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 4) Submit
-    submission_data = {
-        "username": username.strip(),
-        "agent_code": agent_code,
-        "answers": answers_payload,
-    }
+    # 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
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
-        resp = requests.post(submit_url, json=submission_data, timeout=60)
-        resp.raise_for_status()
-        result_data = resp.json()
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
         final_status = (
             f"Submission Successful!\n"
             f"User: {result_data.get('username')}\n"
@@ -91,47 +128,80 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
             f"Message: {result_data.get('message', 'No message received.')}"
         )
-        return final_status, pd.DataFrame(results_log)
+        print("Submission successful.")
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
     except requests.exceptions.HTTPError as e:
-        detail = f"Server responded with status {e.response.status_code}."
+        error_detail = f"Server responded with status {e.response.status_code}."
         try:
-            detail_json = e.response.json()
-            detail += f" Detail: {detail_json.get('detail', e.response.text)}"
-        except Exception:
-            detail += f" Response: {e.response.text[:500]}"
-        return f"Submission Failed: {detail}", pd.DataFrame(results_log)
+            error_json = e.response.json()
+            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
+        except requests.exceptions.JSONDecodeError:
+            error_detail += f" Response: {e.response.text[:500]}"
+        status_message = f"Submission Failed: {error_detail}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
     except requests.exceptions.Timeout:
-        return "Submission Failed: The request timed out.", pd.DataFrame(results_log)
+        status_message = "Submission Failed: The request timed out."
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
     except requests.exceptions.RequestException as e:
-        return f"Submission Failed: Network error - {e}", pd.DataFrame(results_log)
+        status_message = f"Submission Failed: Network error - {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
     except Exception as e:
-        return f"An unexpected error occurred during submission: {e}", pd.DataFrame(results_log)
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
 
 
-# --- Gradio UI ---
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# Advanced Agent Evaluation Runner")
     gr.Markdown(
-        (
-            """
-            **Instructions:**
-            1. Log in with your Hugging Face account.
-            2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run the agent, and submit answers.
-            ---
-            This template is minimal on purpose; extend the agent in agent.py.
-            """
-        )
-    )
+        """
+        **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.
+        """
+    )
     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)
 
-    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
-
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
 
 if __name__ == "__main__":
-    print("Launching Gradio Interface...")
-    demo.launch(debug=True, share=False)
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID")
+
+    if space_host_startup:
+        print(f"✅ SPACE_HOST found: {space_host_startup}")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+
+    if space_id_startup:
+        print(f"✅ SPACE_ID found: {space_id_startup}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+    else:
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
+
+    print("Launching Gradio Interface for Advanced Agent Evaluation...")
+    demo.launch(debug=True, share=False)