Update app.py

app.py

@@ -5,14 +5,19 @@ import inspect
 import time
 import pandas as pd
 from smolagents import DuckDuckGoSearchTool
-# (Keep Constants as is)
+import threading
+from typing import Dict, List, Optional, Tuple
+import json
+
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-
+# --- Global Cache for Answers ---
+cached_answers = {}
+cached_questions = []
+processing_status = {"is_processing": False, "progress": 0, "total": 0}
 
+# --- Basic Agent Definition ---
 class BasicAgent:
     def __init__(self, debug: bool = False):
         self.search = DuckDuckGoSearchTool()
@@ -32,7 +37,7 @@ class BasicAgent:
             time.sleep(1)
             results = self.search(question)
             
-            # Use truthiness check and early return
+            # Use truthfulness check and early return
             if not results:
                 return "No results found for that query."
 
@@ -62,89 +67,180 @@ class BasicAgent:
         
         return answer
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+def fetch_questions() -> Tuple[str, Optional[pd.DataFrame]]:
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    Fetch questions from the API and cache them.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
-    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
-
+    global cached_questions
+    
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent ( modify this part to create your 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}")
     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.")
+            return "Fetched questions list is empty.", None
+            
+        cached_questions = questions_data
+        
+        # Create DataFrame for display
+        display_data = []
+        for item in questions_data:
+            display_data.append({
+                "Task ID": item.get("task_id", "Unknown"),
+                "Question": item.get("question", "")
+            })
+        
+        df = pd.DataFrame(display_data)
+        status_msg = f"Successfully fetched {len(questions_data)} questions. Ready to generate answers."
+        
+        return status_msg, df
+        
     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 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})
-        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}"})
+        return f"An unexpected error occurred: {e}", None
 
-    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)
+def generate_answers_async(progress_callback=None):
+    """
+    Generate answers for all cached questions asynchronously.
+    """
+    global cached_answers, processing_status
+    
+    if not cached_questions:
+        return "No questions available. Please fetch questions first."
+    
+    processing_status["is_processing"] = True
+    processing_status["progress"] = 0
+    processing_status["total"] = len(cached_questions)
+    
+    try:
+        agent = BasicAgent()
+        cached_answers = {}
+        
+        for i, item in enumerate(cached_questions):
+            if not processing_status["is_processing"]:  # Check if cancelled
+                break
+                
+            task_id = item.get("task_id")
+            question_text = item.get("question")
+            
+            if not task_id or question_text is None:
+                continue
+                
+            try:
+                answer = agent(question_text)
+                cached_answers[task_id] = {
+                    "question": question_text,
+                    "answer": answer
+                }
+            except Exception as e:
+                cached_answers[task_id] = {
+                    "question": question_text,
+                    "answer": f"AGENT ERROR: {e}"
+                }
+            
+            processing_status["progress"] = i + 1
+            if progress_callback:
+                progress_callback(i + 1, len(cached_questions))
+                
+    except Exception as e:
+        print(f"Error in generate_answers_async: {e}")
+    finally:
+        processing_status["is_processing"] = False
 
-    # 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)
+def start_answer_generation():
+    """
+    Start the answer generation process in a separate thread.
+    """
+    if processing_status["is_processing"]:
+        return "Answer generation is already in progress.", None
+    
+    if not cached_questions:
+        return "No questions available. Please fetch questions first.", None
+    
+    # Start generation in background thread
+    thread = threading.Thread(target=generate_answers_async)
+    thread.daemon = True
+    thread.start()
+    
+    return "Answer generation started. Check progress below.", None
+
+def get_generation_progress():
+    """
+    Get the current progress of answer generation.
+    """
+    if not processing_status["is_processing"] and processing_status["progress"] == 0:
+        return "Not started", None
+    
+    if processing_status["is_processing"]:
+        progress = processing_status["progress"]
+        total = processing_status["total"]
+        status_msg = f"Generating answers... {progress}/{total} completed"
+        return status_msg, None
+    else:
+        # Generation completed
+        if cached_answers:
+            # Create DataFrame with results
+            display_data = []
+            for task_id, data in cached_answers.items():
+                display_data.append({
+                    "Task ID": task_id,
+                    "Question": data["question"][:100] + "..." if len(data["question"]) > 100 else data["question"],
+                    "Generated Answer": data["answer"][:200] + "..." if len(data["answer"]) > 200 else data["answer"]
+                })
+            
+            df = pd.DataFrame(display_data)
+            status_msg = f"Answer generation completed! {len(cached_answers)} answers ready for submission."
+            return status_msg, df
+        else:
+            return "Answer generation completed but no answers were generated.", None
 
-    # 5. Submit
+def submit_cached_answers(profile: gr.OAuthProfile | None):
+    """
+    Submit the cached answers to the evaluation API.
+    """
+    global cached_answers
+    
+    if not profile:
+        return "Please log in to Hugging Face first.", None
+    
+    if not cached_answers:
+        return "No cached answers available. Please generate answers first.", None
+    
+    username = profile.username
+    space_id = os.getenv("SPACE_ID")
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Unknown"
+    
+    # Prepare submission payload
+    answers_payload = []
+    for task_id, data in cached_answers.items():
+        answers_payload.append({
+            "task_id": task_id,
+            "submitted_answer": data["answer"]
+        })
+    
+    submission_data = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
+    
+    # Submit to API
+    api_url = DEFAULT_API_URL
+    submit_url = f"{api_url}/submit"
+    
     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    
     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"
@@ -152,73 +248,132 @@ 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.')}"
         )
-        print("Submission successful.")
+        
+        # Create results DataFrame
+        results_log = []
+        for task_id, data in cached_answers.items():
+            results_log.append({
+                "Task ID": task_id,
+                "Question": data["question"],
+                "Submitted Answer": data["answer"]
+            })
+        
         results_df = pd.DataFrame(results_log)
         return final_status, results_df
+        
     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:
+        except:
             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: {error_detail}", None
+        
     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: The request timed out.", None
+        
     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}", None
 
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+def clear_cache():
+    """
+    Clear all cached data.
+    """
+    global cached_answers, cached_questions, processing_status
+    cached_answers = {}
+    cached_questions = []
+    processing_status = {"is_processing": False, "progress": 0, "total": 0}
+    return "Cache cleared successfully.", None
+
+# --- Enhanced Gradio Interface ---
+with gr.Blocks(title="Enhanced Agent Evaluation Runner") as demo:
+    gr.Markdown("# Enhanced Agent Evaluation Runner with Answer Caching")
     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.
-
+        **Enhanced Instructions:**
+
+        1. **Clone and Modify**: Clone this space and modify the agent logic as needed.
+        2. **Login**: Log in to your Hugging Face account.
+        3. **Fetch Questions**: Load all questions from the evaluation API.
+        4. **Generate Answers**: Create answers for all questions (runs in background).
+        5. **Review Results**: Check the generated answers before submission.
+        6. **Submit**: Submit your answers when ready.
+
+        **Benefits of this approach:**
+        - ✅ Faster user feedback (separate steps)
+        - ✅ Ability to review answers before submission
+        - ✅ Progress tracking during answer generation
+        - ✅ Cache management for multiple runs
+        
         ---
-        **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.LoginButton()
+    with gr.Row():
+        gr.LoginButton()
+        clear_btn = gr.Button("Clear Cache", variant="secondary")
+
+    with gr.Tab("Step 1: Fetch Questions"):
+        gr.Markdown("### Fetch Questions from API")
+        fetch_btn = gr.Button("Fetch Questions", variant="primary")
+        fetch_status = gr.Textbox(label="Fetch Status", lines=2, interactive=False)
+        questions_table = gr.DataFrame(label="Available Questions", wrap=True)
+        
+        fetch_btn.click(
+            fn=fetch_questions,
+            outputs=[fetch_status, questions_table]
+        )
+
+    with gr.Tab("Step 2: Generate Answers"):
+        gr.Markdown("### Generate Answers (Background Processing)")
+        
+        with gr.Row():
+            generate_btn = gr.Button("Start Answer Generation", variant="primary")
+            refresh_btn = gr.Button("Refresh Progress", variant="secondary")
+        
+        generation_status = gr.Textbox(label="Generation Status", lines=2, interactive=False)
+        answers_preview = gr.DataFrame(label="Generated Answers Preview", wrap=True)
+        
+        generate_btn.click(
+            fn=start_answer_generation,
+            outputs=[generation_status, answers_preview]
+        )
+        
+        refresh_btn.click(
+            fn=get_generation_progress,
+            outputs=[generation_status, answers_preview]
+        )
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    with gr.Tab("Step 3: Submit Results"):
+        gr.Markdown("### Submit Generated Answers")
+        submit_btn = gr.Button("Submit Cached Answers", variant="primary")
+        submission_status = gr.Textbox(label="Submission Status", lines=5, interactive=False)
+        final_results = gr.DataFrame(label="Final Submission Results", wrap=True)
+        
+        submit_btn.click(
+            fn=submit_cached_answers,
+            outputs=[submission_status, final_results]
+        )
 
-    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)
+    # Clear cache functionality
+    clear_btn.click(
+        fn=clear_cache,
+        outputs=[fetch_status, questions_table]
+    )
 
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
+    # Auto-refresh progress every 5 seconds when generation is active
+    demo.load(
+        fn=get_generation_progress,
+        outputs=[generation_status, answers_preview],
+        every=5
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
+    print("\n" + "-"*30 + " Enhanced App Starting " + "-"*30)
+    
     space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+    space_id_startup = os.getenv("SPACE_ID")
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -226,14 +381,14 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    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("-"*(60 + len(" Enhanced App Starting ")) + "\n")
 
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    print("Launching Enhanced Gradio Interface...")
     demo.launch(debug=True, share=False)