Update app.py

app.py

@@ -1,334 +1,209 @@
+""" Basic Agent Evaluation Runner"""
 import os
-import json
-import datetime
-import requests
-from email.utils import parseaddr
-
+import inspect
 import gradio as gr
+import requests
 import pandas as pd
-import numpy as np
-
-from datasets import load_dataset, VerificationMode
-from apscheduler.schedulers.background import BackgroundScheduler
-from huggingface_hub import HfApi
-
-# InfoStrings
-from scorer import question_scorer
-from content import format_error, format_warning, format_log, TITLE, INTRODUCTION_TEXT, SUBMISSION_TEXT, CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, model_hyperlink
+from langchain_core.messages import HumanMessage
+from agent import build_graph
 
-TOKEN = os.environ.get("TOKEN", None)
 
-OWNER="gaia-benchmark"
-DATA_DATASET = f"{OWNER}/GAIA"
-INTERNAL_DATA_DATASET = f"{OWNER}/GAIA_internal"
-SUBMISSION_DATASET = f"{OWNER}/submissions_internal"
-SUBMISSION_DATASET_PUBLIC = f"{OWNER}/submissions_public"
-CONTACT_DATASET = f"{OWNER}/contact_info"
-RESULTS_DATASET = f"{OWNER}/results_public"
-LEADERBOARD_PATH = f"{OWNER}/leaderboard"
-api = HfApi()
 
-YEAR_VERSION = "2023"
-ref_scores_len = {"validation": 165, "test": 301}
-ref_level_len = {"validation": {1: 53, 2: 86, 3: 26}, "test": {1: 93, 2: 159, 3: 49}}
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-os.makedirs("scored", exist_ok=True)
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 
-# Should be False on spaces and True outside
-LOCAL_DEBUG = False #os.environ.get("system", "") != "spaces"
 
-# Display the results
-eval_results = load_dataset(RESULTS_DATASET, YEAR_VERSION, token=TOKEN, download_mode="force_redownload", verification_mode=VerificationMode.NO_CHECKS, trust_remote_code=True)
-contact_infos = load_dataset(CONTACT_DATASET, YEAR_VERSION, token=TOKEN, download_mode="force_redownload", verification_mode=VerificationMode.NO_CHECKS, trust_remote_code=True)
-def get_dataframe_from_results(eval_results, split):
-    local_df = eval_results[split]
-    local_df = local_df.map(lambda row: {"model": model_hyperlink(row["url"], row["model"])})
-    local_df = local_df.remove_columns(["system_prompt", "url"])
-    local_df = local_df.rename_column("model", "Agent name")
-    local_df = local_df.rename_column("model_family", "Model family")
-    local_df = local_df.rename_column("score", "Average score (%)")
-    for i in [1, 2, 3]:
-        local_df = local_df.rename_column(f"score_level{i}", f"Level {i} score (%)")
-    local_df = local_df.rename_column("date", "Submission date")
-    df = pd.DataFrame(local_df)
-    df = df.sort_values(by=["Average score (%)"], ascending=False)
+class BasicAgent:
+    """A langgraph agent."""
+    def __init__(self):
+        print("BasicAgent initialized.")
+        self.graph = build_graph()
 
-    numeric_cols = [c for c in local_df.column_names if "score" in c]
-    df[numeric_cols] = df[numeric_cols].multiply(100).round(decimals=2)
-    #df = df.style.format("{:.2%}", subset=numeric_cols)
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        messages = [HumanMessage(content=question)]
+        result = self.graph.invoke({"messages": messages})
+        answer = result['messages'][-1].content
+        return answer  # kein [14:] mehr nötig!
 
-    return df
 
-#eval_dataframe_val = get_dataframe_from_results(eval_results=eval_results, split="validation")
-eval_dataframe_test = get_dataframe_from_results(eval_results=eval_results, split="test")
 
-# Gold answers
-gold_results = {}
-gold_dataset = load_dataset(INTERNAL_DATA_DATASET, f"{YEAR_VERSION}_all", token=TOKEN, trust_remote_code=True)
-gold_results = {split: {row["task_id"]: row for row in gold_dataset[split]} for split in ["test", "validation"]}
+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 ---
+    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
 
-def restart_space():
-    api.restart_space(repo_id=LEADERBOARD_PATH, token=TOKEN)
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
 
-TYPES = ["markdown", "number", "number", "number", "number", "str", "str", "str"]
-
-def add_new_eval(
-    #val_or_test: str,
-    model: str,
-    model_family: str,
-    system_prompt: str,
-    url: str,
-    path_to_file: str,
-    organisation: str,
-    mail: str,
-    profile: gr.OAuthProfile, 
-):
-    val_or_test = "test"
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        # Was the profile created less than 2 month ago?
-        user_data = requests.get(f"https://huggingface.co/api/users/{profile.username}/overview")
-        creation_date = json.loads(user_data.content)["createdAt"]
-        if datetime.datetime.now() - datetime.datetime.strptime(creation_date, '%Y-%m-%dT%H:%M:%S.%fZ') < datetime.timedelta(days=60):
-            return format_error("This account is not authorized to submit on GAIA.")
-            
-
-        contact_infos = load_dataset(CONTACT_DATASET, YEAR_VERSION, token=TOKEN, download_mode="force_redownload", verification_mode=VerificationMode.NO_CHECKS, trust_remote_code=True)
-        user_submission_dates = sorted(row["date"] for row in contact_infos[val_or_test] if row["username"] == profile.username)
-        if len(user_submission_dates) > 0 and user_submission_dates[-1] == datetime.datetime.today().strftime('%Y-%m-%d'):
-            return format_error("You already submitted once today, please try again tomorrow.")
-
-
-        is_validation = val_or_test == "validation"
-        # Very basic email parsing
-        _, parsed_mail = parseaddr(mail)
-        if not "@" in parsed_mail:
-            return format_warning("Please provide a valid email adress.")
-
-        print("Adding new eval")
-
-        # Check if the combination model/org already exists and prints a warning message if yes
-        if model.lower() in set([m.lower() for m in eval_results[val_or_test]["model"]]) and organisation.lower() in set([o.lower() for o in eval_results[val_or_test]["organisation"]]):
-            return format_warning("This model has been already submitted.")
-        
-        if path_to_file is None:
-            return format_warning("Please attach a file.")
-
-        # SAVE UNSCORED SUBMISSION
-        if LOCAL_DEBUG:
-            print("mock uploaded submission")
-        else:
-            api.upload_file(
-                repo_id=SUBMISSION_DATASET, 
-                path_or_fileobj=path_to_file.name, 
-                path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_{val_or_test}_raw_{datetime.datetime.today()}.jsonl",
-                repo_type="dataset", 
-                token=TOKEN
-            )
-
-        # SAVE CONTACT
-        contact_info = {
-            "model": model,
-            "model_family": model_family,
-            "url": url,
-            "organisation": organisation,
-            "username": profile.username,
-            "mail": mail,
-            "date": datetime.datetime.today().strftime('%Y-%m-%d')
-        }
-        contact_infos[val_or_test]= contact_infos[val_or_test].add_item(contact_info)
-        if LOCAL_DEBUG:
-            print("mock uploaded contact info")
-        else:
-            contact_infos.push_to_hub(CONTACT_DATASET, config_name = YEAR_VERSION, token=TOKEN)
-
-        # SCORE SUBMISSION
-        file_path = path_to_file.name        
-        scores = {"all": 0, 1: 0, 2: 0, 3: 0}
-        num_questions = {"all": 0, 1: 0, 2: 0, 3: 0}
-        task_ids = []
-        with open(f"scored/{organisation}_{model}.jsonl", "w") as scored_file:
-            with open(file_path, 'r') as f:
-                for ix, line in enumerate(f):
-                    try:
-                        task = json.loads(line)
-                    except Exception:
-                        return format_error(f"Line {ix} is incorrectly formatted. Please fix it and resubmit your file.")
-
-                    if "model_answer" not in task:
-                        return format_error(f"Line {ix} contains no model_answer key. Please fix it and resubmit your file.")
-                    answer = task["model_answer"]
-                    task_id = task["task_id"]
-                    try:
-                        level = int(gold_results[val_or_test][task_id]["Level"])
-                    except KeyError:
-                        return format_error(f"{task_id} not found in split {val_or_test}. Are you sure you submitted the correct file?")
-
-                    score = question_scorer(task['model_answer'], gold_results[val_or_test][task_id]["Final answer"])
-                    
-                    scored_file.write(
-                        json.dumps({
-                            "id": task_id,
-                            "model_answer": answer,
-                            "score": score,
-                            "level": level
-                        }) + "\n"
-                    )
-                    task_ids.append(task_id)
-
-                    scores["all"] += score
-                    scores[level] += score
-                    num_questions["all"] += 1
-                    num_questions[level] += 1
-
-        # Check if there's any duplicate in the submission
-        if len(task_ids) != len(set(task_ids)):
-            return format_error("There are duplicates in your submission. Please check your file and resubmit it.")
-
-        if any([num_questions[level] != ref_level_len[val_or_test][level] for level in [1, 2, 3]]):
-            return format_error(f"Your submission has {num_questions[1]} questions for level 1, {num_questions[2]} for level 2, and {num_questions[3]} for level 3, but it should have {ref_level_len[val_or_test][1]}, {ref_level_len[val_or_test][2]}, and {ref_level_len[val_or_test][3]} respectively. Please check your submission.")
-
-        # SAVE SCORED SUBMISSION
-        if LOCAL_DEBUG:
-            print("mock uploaded scored submission")
-        else:
-            api.upload_file(
-                repo_id=SUBMISSION_DATASET, 
-                path_or_fileobj=f"scored/{organisation}_{model}.jsonl",
-                path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_{val_or_test}_scored_{datetime.datetime.today()}.jsonl", 
-                repo_type="dataset", 
-                token=TOKEN
-            )
-
-            # Save scored file
-            if is_validation:
-                api.upload_file(
-                    repo_id=SUBMISSION_DATASET_PUBLIC, 
-                    path_or_fileobj=f"scored/{organisation}_{model}.jsonl",
-                    path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_{val_or_test}_scored_{datetime.datetime.today()}.jsonl", 
-                    repo_type="dataset", 
-                    token=TOKEN
-                )
-
-        # SAVE TO LEADERBOARD DATA
-        eval_entry = {
-            "model": model,
-            "model_family": model_family,
-            "system_prompt": system_prompt,
-            "url": url,
-            "organisation": organisation,
-            "score": scores["all"]/ref_scores_len[val_or_test],
-            "score_level1": scores[1]/num_questions[1],
-            "score_level2": scores[2]/num_questions[2],
-            "score_level3": scores[3]/num_questions[3],
-            "date": datetime.datetime.today().strftime('%Y-%m-%d')
-        }
-        if num_questions[1] + num_questions[2] + num_questions[3] != ref_scores_len[val_or_test]:
-            return format_error(f"Your submission has {len(scores['all'])} questions for the {val_or_test} set, but it should have {ref_scores_len[val_or_test]}. Please check your submission.")
-        # Catching spam submissions of 100%
-        if all((eval_entry[k] == 1 for k in ["score_level1", "score_level2", "score_level3"])):
-            return format_error(f"There was a problem with your submission. Please open a discussion.")
-
-        # Testing for duplicates - to see if we want to add something like it as it would allow people to try to see the content of other submissions
-        #eval_entry_no_date = {k: v for k, v in eval_entry if k != "date"}
-        #columns_no_date = [c for c in eval_results[val_or_test].column_names if c != "date"]
-        #if eval_entry_no_date in eval_results[val_or_test].select_columns(columns_no_date):
-        #    return format_error(f"Your submission is an exact duplicate from an existing submission.")
-
-        eval_results[val_or_test] = eval_results[val_or_test].add_item(eval_entry)
-        print(eval_results)
-        if LOCAL_DEBUG:
-            print("mock uploaded results to lb")
-        else:
-            eval_results.push_to_hub(RESULTS_DATASET, config_name = YEAR_VERSION, token=TOKEN)
-
-
-        return format_log(f"Model {model} submitted by {organisation} successfully.\nPlease wait a few hours and refresh the leaderboard to see your score displayed.")
+        agent = BasicAgent()
     except Exception as e:
-        print(e)
-        return format_error(f"An error occurred, please open a discussion and indicate at what time you encountered the error.\n")
-
-
-def refresh():
-    eval_results = load_dataset(RESULTS_DATASET, YEAR_VERSION, token=TOKEN, download_mode="force_redownload", verification_mode=VerificationMode.NO_CHECKS,trust_remote_code=True)
-    #eval_dataframe_val = get_dataframe_from_results(eval_results=eval_results, split="validation")
-    eval_dataframe_test = get_dataframe_from_results(eval_results=eval_results, split="test")
-    return eval_dataframe_test
-
-def upload_file(files):
-    file_paths = [file.name for file in files]
-    return file_paths
-
+        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.")
+    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}"})
+
+    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. 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:
+        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.')}"
+        )
+        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
+    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.
+        """
+    )
 
-demo = gr.Blocks()
-with demo:
-    gr.HTML(TITLE)
-    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+    gr.LoginButton()
 
-    with gr.Row():
-        with gr.Accordion("📙 Citation", open=False):
-            citation_button = gr.Textbox(
-                value=CITATION_BUTTON_TEXT,
-                label=CITATION_BUTTON_LABEL,
-                elem_id="citation-button",
-            ) #.style(show_copy_button=True)
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
 
-    gr.Markdown("Results: Test")
-    leaderboard_table_test = gr.components.Dataframe(
-        value=eval_dataframe_test, datatype=TYPES, interactive=False,
-        column_widths=["20%"] 
-    )
-    #with gr.Tab("Results: Validation"):
-    #    leaderboard_table_val = gr.components.Dataframe(
-    #        value=eval_dataframe_val, datatype=TYPES, interactive=False,
-    #        column_widths=["20%"] 
-    #    )
+    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)
 
-    refresh_button = gr.Button("Refresh")
-    refresh_button.click(
-        refresh,
-        inputs=[],
-        outputs=[
-            #leaderboard_table_val,
-            leaderboard_table_test,
-        ],
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
     )
-    with gr.Accordion("Submit a new model for evaluation"):
-        with gr.Row():
-            gr.Markdown(SUBMISSION_TEXT, elem_classes="markdown-text")
-        with gr.Row():
-            with gr.Column():
-                #level_of_test = gr.Radio(["test"], value="test", label="Split")
-                model_name_textbox = gr.Textbox(label="Agent name")
-                model_family_textbox = gr.Textbox(label="Model family")
-                system_prompt_textbox = gr.Textbox(label="System prompt example")
-                url_textbox = gr.Textbox(label="Url to model information")
-            with gr.Column():
-                organisation = gr.Textbox(label="Organisation")
-                mail = gr.Textbox(label="Contact email (will be stored privately, & used if there is an issue with your submission)")
-                file_output = gr.File()
-
-
-        with gr.Row():
-            gr.LoginButton()
-            submit_button = gr.Button("Submit Eval On Test")
-        submission_result = gr.Markdown()
-        submit_button.click(
-            add_new_eval,
-            [
-                #level_of_test,
-                model_name_textbox,
-                model_family_textbox,
-                system_prompt_textbox,
-                url_textbox,
-                file_output,
-                organisation,
-                mail
-            ],
-            submission_result,
-        )
 
-scheduler = BackgroundScheduler()
-scheduler.add_job(restart_space, "interval", seconds=3600)
-scheduler.start()
-demo.launch(debug=True)
+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?).")
+
+    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")
+    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 Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)