update the lesson

app.py

@@ -1,196 +1,171 @@
+# --- Standard Library Imports ---
 import os
-import gradio as gr
 import requests
-import inspect
 import pandas as pd
+import gradio as gr
+from typing import Union
 
-# (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 ------
+# You should modify this class to improve agent behavior.
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        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}")
+        # Print the incoming question (preview)
+        print(f"🤖 Agent received question: {question[:50]}...")
+        
+        # Your logic goes here — modify as needed.
+        fixed_answer = "This is a default answer."  # You can replace this with dynamic generation.
+        
+        print(f"📤 Agent returns: {fixed_answer}")
         return fixed_answer
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+
+# --- Core Evaluation Logic ---
+def run_and_submit_all(profile: Union[gr.OAuthProfile, None]):
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    Core function that:
+    - Initializes the agent
+    - Fetches questions
+    - Generates answers
+    - Submits them to the scoring API
+    - Returns the final result and answers DataFrame
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    space_id = os.getenv("SPACE_ID")  # Optional but used to link to the repo
 
     if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
+        username = profile.username
+        print(f"👤 Logged in user: {username}")
     else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
+        print("⚠️ User not logged in.")
+        return "Please login using Hugging Face Login button.", None
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Not available"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    # 1. Instantiate the agent
     try:
         agent = BasicAgent()
     except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {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
-    print(f"Fetching questions from: {questions_url}")
+        return f"❌ Error initializing agent: {e}", None
+
+    # 2. Fetch questions
+    print(f"📥 Fetching questions from: {questions_url}")
     try:
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
-             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
+            return "❌ Fetched questions list is empty or invalid.", None
+        print(f"✅ {len(questions_data)} questions fetched.")
     except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
+        return f"❌ Error fetching questions: {e}", None
 
-    # 3. Run your Agent
+    # 3. Run the agent on all questions
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
+
+    print("🧠 Running agent on 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:
-             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}"})
+            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)
+        return "❌ Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 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)
+    # 4. Prepare Submission
+    submission_data = {
+        "username": username,
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
+    print(f"🚀 Submitting {len(answers_payload)} answers...")
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    # 5. Submit answers to scoring endpoint
     try:
         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"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
+            f"✅ Submission Successful!\n"
+            f"👤 User: {result_data.get('username')}\n"
+            f"📊 Score: {result_data.get('score')}% "
+            f"({result_data.get('correct_count')}/{result_data.get('total_attempted')} correct)\n"
+            f"📝 Message: {result_data.get('message', 'No message.')}"
         )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
+        return final_status, pd.DataFrame(results_log)
+
     except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            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
+        return f"❌ Submission Failed (HTTP error): {e}", pd.DataFrame(results_log)
     except requests.exceptions.Timeout:
-        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:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+        return "❌ Submission Failed: Request timed out.", pd.DataFrame(results_log)
     except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+        return f"❌ Submission Failed: {e}", pd.DataFrame(results_log)
 
 
-# --- Build Gradio Interface using Blocks ---
+# --- Gradio UI Setup ---
 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. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
-        """
-    )
-
+    gr.Markdown("# 🤖 Basic Agent Evaluation Tool")
+    gr.Markdown("""
+    ### 🛠 Instructions:
+    1. Clone this Hugging Face Space.
+    2. Implement your own logic in the `BasicAgent` class.
+    3. Login with your Hugging Face account.
+    4. Press the button to run all questions through your agent and submit.
+
+    **Note:** It may take some time depending on the number of questions and agent logic.
+    """)
+
+    # Login and button interface
     gr.LoginButton()
+    run_button = gr.Button("▶️ Run Evaluation & Submit All Answers")
+    status_output = gr.Textbox(label="📝 Submission Status", lines=5, interactive=False)
+    results_table = gr.DataFrame(label="📄 Agent Answers Log", wrap=True)
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+    # Hook button click to function
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
+# --- Local App Runner ---
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    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?).")
+    print("\n" + "-" * 30 + " 🚀 App Starting " + "-" * 30)
+    space_host = os.getenv("SPACE_HOST")
+    space_id = os.getenv("SPACE_ID")
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        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")
+    if space_host:
+        print(f"✅ SPACE_HOST: {space_host}")
+        print(f"🌍 App URL: https://{space_host}.hf.space")
     else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+        print("ℹ️  SPACE_HOST not found (running locally?)")
 
-    print("-"*(60 + len(" App Starting ")) + "\n")
+    if space_id:
+        print(f"✅ SPACE_ID: {space_id}")
+        print(f"📦 Repo: https://huggingface.co/spaces/{space_id}")
+    else:
+        print("ℹ️  SPACE_ID not set")
 
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    print("-" * 60)
+    print("🔧 Launching Gradio app...")
+    demo.launch(debug=True, share=False)