app.py

from smolagents import CodeAgent, DuckDuckGoSearchTool, VisitWebpageTool, FinalAnswerTool, InferenceClientModel, tool
import datetime
import requests
import pytz
import yaml
import json
import os
import openpyxl
import whisper
import gradio as gr
import inspect
import pandas as pd

# --- constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- tools ---
web_search = DuckDuckGoSearchTool()
visit_webpage = VisitWebpageTool()
final_answer = FinalAnswerTool()

def _download_file(file_name: str) -> None:
    if not os.path.exists(file_name):
        url = f"{DEFAULT_API_URL}/files/{file_name.split('.')[-2]}"
        r = requests.get(url)
        with open(file_name, "wb") as f:
            f.write(r.content)

@tool
def read_file_as_text(file_name: str) -> str:
    """
    Opens a file and returns its content as readable text.
    Supports 'txt', 'json', 'csv', 'xlsx', and 'mp3' (for mp3, it transcribes speech to text).
    Args:
        file_name (str): The path or name of the file.
    Returns:
        str: The content of the file as text, or transcribed speech if 'mp3'.
    """
    _download_file(file_name)
    file_type = file_name.split(".")[-1]
    try:
        if file_type in {"txt", "py"}:
            with open(file_name, "r", encoding="utf-8") as f:
                return f.read()
        elif file_type == "json":
            with open(file_name, "r", encoding="utf-8") as f:
                data = json.load(f)
            return json.dumps(data, indent=2)
        elif file_type == "csv":
            with open(file_name, "r", encoding="utf-8") as f:
                reader = csv.reader(f)
                rows = list(reader)
            return "\n".join([", ".join(row) for row in rows])
        elif file_type == "xlsx":
            wb = openpyxl.load_workbook(file_name, data_only=True)
            sheet = wb.active
            content = []
            for row in sheet.iter_rows(values_only=True):
                content.append(", ".join(str(cell) if cell is not None else "" for cell in row))
            return "\n".join(content)
        elif file_type == "mp3":
            w = whisper.load_model("base")
            res = w.transcribe(file_name)
            return res["text"]
        else:
            return f"File type '{file_type}' not supported."
    except FileNotFoundError:
        return f"File '{file_name}' not found."
    except Exception as e:
        return f"Error opening file '{file_name}': {str(e)}"

@tool
def reverse_string(text: str) -> str:
    """
    Reverses the input text.
    Args:
        text (str): The input string to be reversed.
    Returns:
        str: The reversed string.
    """
    return text[::-1]


# --- agent ---
class Agent:
    def __init__(self, model_id, provider, tools, auth_imports):
        self.model_id = "Qwen/Qwen3-235B-A22B-Thinking-2507"
        self.provider = "auto"
        self.tools = [read_file_as_text, reverse_string, web_search, visit_webpage, final_answer]
        self.auth_imports = ["pandas", "numpy", "datetime", "json", "re", "math", "os", "requests", "csv", "urllib"]
        self.max_steps = 5
        self.agent = CodeAgent(
            model=InferenceClientModel(model_id=self.model_id, provider=self.provider, token=os.getenv("HF_TOKEN")),
            tools=self.tools,
            additional_authorized_imports=self.auth_imports,
            max_steps=self.max_steps,
        )

    def __call__(self, question_text, file_name) -> str:
        template = t"""You are a highly precise question-answering agent.
            When given a question:
            - If necessary, perform a web search using the tool `web_search` to find possible sources of information.
            - If the web search only returns titles and short snippets, you MUST visit the actual webpage using the `visit_webpage` tool to read the full content before answering.
            - If the task requires reading, listening, or analyzing a file, you must use the file specified after the question, NOT the file name mentioned casually inside the question text.
            - Comma separated lists MUST contain a single space after each comma.
            - If you are asked for a number, don't use comma to write your number, nor use units such as $ or percent sign unless specified otherwise.
            - If you are asked for a string, don't use articles, nor abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise.
            - If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
            - Only answer after you have gathered enough information by reading the actual page contents.
            - Once you have the final answer, you must call `final_answer("your_answer")` immediately after printing it.
            - Do not retry or execute anything else after calling `final_answer`.
            - `final_answer` must wrap the exact printed value.
            - Provide ONLY the precise answer requested. 
            - Do not include explanations, steps, reasoning, or additional text when calling `final_answer`. 
            - Be direct and specific. The GAIA benchmark requires exactly matching answers.
            
            Based on the above guidelines, answer the following question:
            -- beginning of question --
            {question_text}
            -- end of question --
            If the questions mentions the need to use a file, use the following `file_name` value below as the `file_name` parameter in any function calls: 
            file_name: {file_name}"""
        enhanced_question = template.substitute(question_text=question_text, file_name=file_name)
        response = self.agent.run(enhanced_question)
        return response


# --- submission code ---
def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the PromptedAgent 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

    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 = PromptedAgent()
    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.")
    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")
        file_name = item.get("file_name")
        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, file_name)
            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.
        """
    )

    gr.LoginButton()

    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]
    )

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)