Implemented Step-by-Step Evaluation for questions and download/upload for progress

app.py

@@ -1,202 +1,373 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+# Ensure basic_agent.py is in the same directory
 from basic_agent import BasicAgent
+import json
+import tempfile
 
-
-# (Keep Constants as is)
 # --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-NO_SUBMIT = True
-
-
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    # Get the SPACE_ID for sending link to the code
-    space_id = os.getenv("SPACE_ID")
+DEFAULT_API_URL = os.getenv(
+    "API_URL", "https://agents-course-unit4-scoring.hf.space")
+QUESTIONS_URL = f"{DEFAULT_API_URL}/questions"
+SUBMIT_URL = f"{DEFAULT_API_URL}/submit"
+NO_SUBMIT = False
+PLACEHOLDER_UNATTEMPTED = "_NOT_ATTEMPTED_"
 
-    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
+# --- Agent Instantiation Helper ---
 
-    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)
+def get_agent_instance():
     try:
-        agent = BasicAgent()
+        return 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)
+        gr.Warning(f"Error initializing agent: {e}")
+        return None
+
+# --- Original run_and_submit_all function ---
 
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    space_id = os.getenv("SPACE_ID")
+    if not profile:
+        gr.Warning("Please Login first.")
+        return "Login required.", pd.DataFrame()
+    username = profile.username
+    print(f"User logged in: {username}")
+    agent = get_agent_instance()
+    if not agent:
+        return "Failed to initialize agent.", pd.DataFrame()
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "local_run"
+
+    print(f"Fetching questions from: {QUESTIONS_URL}")
     try:
-        response = requests.get(questions_url, timeout=15)
+        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
+            return "Fetched questions list is empty.", pd.DataFrame()
         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
+        return f"Error fetching/decoding questions: {e}", pd.DataFrame()
 
-    # 3. Run your Agent
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data[:5]:
-        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}")
+    print(f"Running agent on all {len(questions_data)} questions...")
+    for item in questions_data:
+        task_id, q_text = item.get("task_id"), item.get("question")
+        if not task_id or q_text is None:
+            print(f"Skipping item: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)
+            print(f"Running agent for Task ID {task_id}...")
+            submitted_answer = agent(q_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})
+                {"Task ID": task_id, "Question": q_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}"})
-
+                {"Task ID": task_id, "Question": q_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+    results_df = pd.DataFrame(results_log, columns=[
+                              "Task ID", "Question", "Submitted Answer"])  # Ensure column order
     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 produced no answers.", results_df
 
-    # 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)
+    if NO_SUBMIT:
+        return f"Submission SKIPPED (NO_SUBMIT=True). {len(answers_payload)} answers prepared.", results_df
+    print(f"Submitting {len(answers_payload)} answers to: {SUBMIT_URL}")
+    try:
+        response = requests.post(
+            SUBMIT_URL, json=submission_data, timeout=max(60, len(answers_payload) * 2))
+        response.raise_for_status()
+        result_data = response.json()
+        return (f"Submission Successful! User: {result_data.get('username')}, "
+                f"Score: {result_data.get('score', 'N/A')}% ({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')}), "
+                f"Msg: {result_data.get('message', '')}"), results_df
+    except Exception as e:
+        return f"Submission Failed: {e}", results_df
+
+# --- Step-by-Step Action Functions ---
+
+
+def load_questions_action(profile: gr.OAuthProfile | None):
+    if not profile:
+        gr.Warning("Please Login first.")
+        return "Login required.", [], pd.DataFrame(), None
+    print(f"Fetching questions for {profile.username} from: {QUESTIONS_URL}")
+    try:
+        response = requests.get(QUESTIONS_URL, timeout=15)
+        response.raise_for_status()
+        questions_server_data = response.json()
+        if not questions_server_data:
+            return "Fetched questions list is empty.", [], pd.DataFrame(), None
+
+        new_results_log = [
+            {"Task ID": q.get("task_id"), "Question": q.get(
+                "question"), "Submitted Answer": PLACEHOLDER_UNATTEMPTED}
+            for q in questions_server_data if q.get("task_id") and q.get("question") is not None
+        ]
+
+        msg = f"Fetched {len(new_results_log)} questions. Progress reset."
+        gr.Info(msg)
+        return (
+            msg,
+            # For results_log_list_state (this is the single source of truth now)
+            new_results_log,
+            pd.DataFrame(new_results_log, columns=[
+                         "Task ID", "Question", "Submitted Answer"]),  # For results_display_table
+            None  # For q_number_input (reset selection)
+        )
+    except Exception as e:
+        msg = f"Error fetching questions: {e}"
+        gr.Error(msg)
+        return msg, [], pd.DataFrame(), None
+
+
+def run_single_question_action(profile: gr.OAuthProfile | None, q_idx: int | None, current_results_log: list):
+    if not profile:
+        gr.Warning("Please Login first.")
+        return "Login required.", current_results_log, pd.DataFrame(current_results_log)
+    # current_results_log is results_log_list_state, which has 'Task ID', 'Question', 'Submitted Answer'
+    if not current_results_log:
+        gr.Warning("No questions loaded.")
+        return "No questions loaded.", current_results_log, pd.DataFrame(current_results_log)
+    if q_idx is None:
+        gr.Warning("Select question or enter index.")
+        return "Invalid index.", current_results_log, pd.DataFrame(current_results_log)
+    if not 0 <= q_idx < len(current_results_log):
+        return f"Index {q_idx} out of bounds.", current_results_log, pd.DataFrame(current_results_log)
+
+    agent = get_agent_instance()
+    if not agent:
+        return "Agent init failed.", current_results_log, pd.DataFrame(current_results_log)
+
+    # Get question details from the selected row in current_results_log
+    item_to_process = current_results_log[q_idx]
+    task_id, q_text = item_to_process.get(
+        "Task ID"), item_to_process.get("Question")
+    if not task_id or q_text is None:
+        return f"Invalid question data at index {q_idx}.", current_results_log, pd.DataFrame(current_results_log)
+
+    print(f"Running for Task ID {task_id} (Index {q_idx}): {q_text[:50]}...")
+    try:
+        submitted_answer = agent(q_text)
+        status_msg = f"Successfully processed Task ID {task_id}."
+    except Exception as e:
+        submitted_answer = f"AGENT ERROR: {e}"
+        status_msg = f"Error on task {task_id}: {e}"
+        gr.Error(status_msg)
+
+    updated_results_log = list(current_results_log)  # Make a mutable copy
+    updated_results_log[q_idx] = {
+        "Task ID": task_id, "Question": q_text, "Submitted Answer": submitted_answer}
+
+    gr.Info(status_msg if "AGENT ERROR" not in submitted_answer else "Agent run finished with error.")
+    return status_msg, updated_results_log, pd.DataFrame(updated_results_log, columns=["Task ID", "Question", "Submitted Answer"])
+
+
+def download_progress_action(results_log_list: list):
+    if not results_log_list:
+        gr.Info("No progress to download.")
+        return None
+    try:
+        with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".json", encoding='utf-8') as tmpfile:
+            json.dump(results_log_list, tmpfile, indent=2)
+        gr.Info("Progress file ready.")
+        return gr.File(value=tmpfile.name, label="progress.json")
+    except Exception as e:
+        gr.Error(f"Error preparing download: {e}")
+        return None
+
+
+def load_progress_action(uploaded_file_obj):
+    if uploaded_file_obj is None:
+        gr.Warning("No file uploaded.")
+        return "No file.", [], pd.DataFrame(), None
+    try:
+        with open(uploaded_file_obj.name, "r", encoding='utf-8') as f:
+            loaded_data = json.load(f)
+        if not isinstance(loaded_data, list) or \
+           not all(isinstance(item, dict) and all(k in item for k in ["Task ID", "Question", "Submitted Answer"]) for item in loaded_data):
+            raise ValueError(
+                "Invalid file format. Expects list of {'Task ID': ..., 'Question': ..., 'Submitted Answer': ...}")
+
+        new_results_log_list = loaded_data
+        msg = f"Loaded {len(new_results_log_list)} entries from file."
+        gr.Info(msg)
+        return (
+            msg,
+            new_results_log_list,
+            pd.DataFrame(new_results_log_list, columns=[
+                         "Task ID", "Question", "Submitted Answer"]),
+            None  # Reset selected index
+        )
+    except Exception as e:
+        msg = f"Error loading progress: {e}"
+        gr.Error(msg)
+        return msg, [], pd.DataFrame(), None
+
+
+def submit_current_results_action(profile: gr.OAuthProfile | None, results_log_list: list):
+    if not profile:
+        gr.Warning("Please Login first.")
+        return "Login required."
+    username = profile.username
+    if not results_log_list:
+        return "No results to submit."
 
-    # 5. Submit
+    space_id = os.getenv("SPACE_ID")
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "local_run"
+
+    answers_payload = [
+        {"task_id": e["Task ID"], "submitted_answer": e["Submitted Answer"]}
+        for e in results_log_list
+        if e["Submitted Answer"] != PLACEHOLDER_UNATTEMPTED and "AGENT ERROR" not in str(e.get("Submitted Answer", ""))
+    ]
+    if not answers_payload:
+        return "No attempted (non-error) answers to submit."
+
+    submission_data = {"username": username.strip(
+    ), "agent_code": agent_code, "answers": answers_payload}
     if NO_SUBMIT:
-        return
+        return f"Submission SKIPPED. {len(answers_payload)} answers ready."
 
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    gr.Info(f"Submitting {len(answers_payload)} answers for '{username}'...")
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response = requests.post(
+            SUBMIT_URL, json=submission_data, timeout=max(60, len(answers_payload)*2))
         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.')}"
-        )
-        print("Submission successful.")
-        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:
-            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:
-        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 (f"Submission Successful! User: {result_data.get('username')}, Score: {result_data.get('score', 'N/A')}% "
+                f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')}), Msg: {result_data.get('message', '')}")
     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
-
-
-# --- Build Gradio Interface using Blocks ---
-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.
-        """
-    )
+        return f"Submission Failed: {e}"
+
+
+# --- Build Gradio Interface ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# Enhanced Agent Evaluation Runner")
+    # ... Instructions markdown ...
+
+    # Single source of truth for the state of all questions and their answers
+    results_log_list_state = gr.State([])
 
     gr.LoginButton()
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    with gr.Tabs():
+        with gr.TabItem("Step-by-Step Evaluation"):
+            gr.Markdown("## Evaluation Workflow")
+
+            with gr.Row():
+                load_questions_button = gr.Button(
+                    "1. Load Questions from Server", variant="secondary")
+            load_q_status = gr.Textbox(
+                label="Load Status", interactive=False, lines=1)
+
+            gr.Markdown("### 2. Select a Question and Run Agent")
+            # This table is now the main display for questions and answers
+            results_display_table = gr.DataFrame(
+                label="Questions & Answers (Select row to run agent)",
+                headers=["Task ID", "Question", "Submitted Answer"],
+                row_count=10,
+                wrap=True,
+                interactive=True  # Allows row selection
+            )
+            with gr.Row():
+                q_number_input = gr.Number(
+                    label="Selected Question Index", minimum=0, precision=0, step=1, value=None, interactive=True)
+                run_single_q_button = gr.Button(
+                    "Run Agent for Selected Index", variant="primary")
+            single_q_status = gr.Textbox(
+                label="Run Single Status", interactive=False, lines=1)
+
+            with gr.Accordion("3. Manage Full Progress (Download/Upload)", open=False):
+                download_file_output = gr.File(
+                    label="Download Link", interactive=False)
+                download_button = gr.Button("Download All Progress")
+                with gr.Row():
+                    upload_file_input = gr.File(
+                        label="Upload Progress File (JSON)", type="filepath", file_types=[".json"])
+                    load_progress_button = gr.Button("Load Uploaded File")
+                upload_status = gr.Textbox(
+                    label="Upload Status", interactive=False, lines=1)
+
+            gr.Markdown("### 4. Submit Results")
+            submit_step_by_step_button = gr.Button(
+                "Submit Attempted Answers", variant="primary")
+            submit_sbs_status = gr.Textbox(
+                label="Submission Status", lines=3, interactive=False)
+
+        with gr.TabItem("Run All & Submit (Original Batch)"):
+            gr.Markdown("## Original Batch Runner")
+            original_run_button = gr.Button(
+                "Run All Questions & Submit", variant="primary")
+            original_status_output = gr.Textbox(
+                label="Batch Run Status / Result", lines=3, interactive=False)
+            original_results_table = gr.DataFrame(label="Batch Run Q&A", wrap=True, interactive=False, headers=[
+                                                  "Task ID", "Question", "Submitted Answer"])
+
+    # --- Wire up Step-by-Step controls ---
+    load_questions_button.click(
+        fn=load_questions_action, inputs=[],
+        outputs=[load_q_status, results_log_list_state,
+                 results_display_table, q_number_input]
+    )
 
-    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)
+    def handle_select_question_from_results_table(evt: gr.SelectData):
+        if evt.index is not None:
+            # evt.index should be the row index (int) for single row selection
+            # If it's a tuple (row, col) for cell selection, take index[0]
+            if isinstance(evt.index, tuple):
+                return evt.index[0]
+            elif isinstance(evt.index, int):
+                return evt.index
+            # Handle list for multi-select if it were enabled (take first)
+            elif isinstance(evt.index, list) and evt.index:
+                return evt.index[0]
+        return None  # No change or clear if no valid selection
 
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
+    results_display_table.select(
+        fn=handle_select_question_from_results_table, inputs=None, outputs=[q_number_input], show_progress="hidden"
     )
 
+    run_single_q_button.click(
+        fn=run_single_question_action,
+        inputs=[q_number_input, results_log_list_state],
+        outputs=[single_q_status, results_log_list_state, results_display_table]
+    )
+    download_button.click(download_progress_action, [
+                          results_log_list_state], [download_file_output])
+    load_progress_button.click(
+        load_progress_action, [upload_file_input],
+        [upload_status, results_log_list_state,
+            results_display_table, q_number_input]
+    )
+    submit_step_by_step_button.click(
+        submit_current_results_action, [
+            results_log_list_state], [submit_sbs_status]
+    )
+
+    original_run_button.click(run_and_submit_all, [], [
+                              original_status_output, original_results_table])
+
 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
-
+    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")
+            f"✅ SPACE_HOST: {space_host_startup}, URL: https://{space_host_startup}.hf.space")
     else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    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("ℹ️  SPACE_HOST not found (local run?).")
+    if space_id_startup:
         print(
-            f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+            f"✅ SPACE_ID: {space_id_startup}, Repo: https://huggingface.co/spaces/{space_id_startup}")
     else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
+        print("ℹ️  SPACE_ID not found. Repo URL cannot be determined.")
     print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True)