app.py

import inspect
import json
import os
from io import BytesIO
from typing import Optional

import gradio as gr
import pandas as pd
import requests
import whisper
from bs4 import BeautifulSoup, NavigableString, Tag
from PIL import Image
from smolagents import (
    CodeAgent,
    GoogleSearchTool,
    InferenceClientModel,
    load_tool,
    OpenAIServerModel,
    tool,
    Tool,
    ToolCollection,
    VisitWebpageTool,
    WikipediaSearchTool,
)

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"


@tool
def extract_table_from_html(html: str, match: str | None = None) -> list:
    """
    A tool that extracts HTML tables from HTML content and returns them as pandas DataFrames.
    Example usecases include extracting tables from Wikipedia pages, HTML emails, or other web content.
    Args:
        html (str): The HTML content containing HTML tables to extract. This can be raw HTML
                   string content or a URL to a webpage.
        match (str | None, optional): A string or regular expression pattern to match
                                    against table text content. If None, all tables
                                    are extracted. Defaults to None.
                                    DO NOT use HTML strings / tags in this parameter.

    Returns:
        list: A list of pandas DataFrames, where each DataFrame represents a table found
              in the HTML content. Returns an empty list if no tables are found.
    """
    import pandas as pd

    try:
        # Extract tables using pandas
        if match is not None:
            tables = pd.read_html(html, match=match)
        else:
            tables = pd.read_html(html)

        # Return the list of DataFrames directly
        return tables if tables else []

    except ValueError as e:
        if "No tables found" in str(e):
            # Return empty list instead of raising error
            return []
        else:
            raise ValueError(f"Error extracting tables from HTML content: {e}")
    except Exception as e:
        raise Exception(f"Failed to extract tables from HTML content: {e}")


@tool
def audio_to_text(file_path: str) -> str:
    """
    A tool that converts audio files to text using OpenAI's Whisper speech recognition model.

    This function transcribes audio content from a local audio file and returns the transcript
    as a JSON string containing timestamped segments. It uses the Whisper "base" model for
    speech-to-text conversion.

    Args:
        file_path (str): The local file path to the audio file to be transcribed.
                        Supports common audio formats like MP3, WAV, M4A, FLAC, etc.

    Returns:
        str: A JSON string containing the transcript data with the following structure:
             {
                 "transcript": [
                     {
                         "start": float,  # Start time in seconds
                         "end": float,    # End time in seconds
                         "text": str      # Transcribed text segment
                     },
                     ...
                 ]
             }

    Raises:
        FileNotFoundError: If the specified audio file does not exist.
        Exception: If the audio file cannot be processed or transcribed.

    Example:
        >>> result = audio_to_text("path/to/audio.mp3")
        >>> import json
        >>> transcript_data = json.loads(result)
        >>> for segment in transcript_data["transcript"]:
        ...     print(f"{segment['start']:.2f}s - {segment['end']:.2f}s: {segment['text']}")

    Note:
        - Uses OpenAI Whisper "base" model for transcription
        - Processes audio without verbose output or word-level timestamps
        - Returns empty segments list if no speech is detected
        - Processing time depends on audio file length and system performance
    """
    import json

    import whisper

    model = whisper.load_model("base")
    result = model.transcribe(file_path, verbose=False, word_timestamps=False)

    transcript_data = [
        {
            "start": segment["start"],
            "end": segment["end"],
            "text": segment["text"].strip(),
        }
        for segment in result["segments"]
    ]

    return json.dumps({"transcript": transcript_data})


@tool
def get_wikipedia_page_url_by_year(wikipedia_page_name: str, year: int) -> str:
    """
    Retrieve Wikipedia page URL for a specific year (latest revision in that year).

    Args:
        wikipedia_page_name (str): Name of the Wikipedia page
        year (int): Year to get the page content from

    Returns:
        str: URL of the Wikipedia page from that year with revision included
    """
    import requests
    import wikipediaapi

    # Create Wikipedia API instance
    wiki = wikipediaapi.Wikipedia(
        user_agent="Final Project Agent Course (vthanhvinh@gmail.com)",
        language="en",
    )

    # Get the page
    page = wiki.page(wikipedia_page_name)
    if not page.exists():
        raise ValueError(f"Wikipedia page '{wikipedia_page_name}' does not exist")

    # Use Wikipedia API to get revisions from the specified year
    api_url = "https://en.wikipedia.org/w/api.php"

    # Get the latest revision from the specified year
    params = {
        "action": "query",
        "format": "json",
        "prop": "revisions",
        "titles": wikipedia_page_name,
        "rvprop": "ids|timestamp",
        "rvend": f"{year}-12-31T23:59:59Z",
        "rvstart": f"{year}-01-01T00:00:00Z",
        "rvdir": "newer",
        "rvlimit": 1,
    }

    response = requests.get(api_url, params=params)
    data = response.json()
    pages = data["query"]["pages"]
    page_id = list(pages.keys())[0]
    revisions = pages[page_id].get("revisions", [])

    if not revisions:
        raise ValueError(
            f"No revisions found for '{wikipedia_page_name}' in year {year}"
        )

    # Get revision ID and construct URL
    rev_id = revisions[0]["revid"]
    url = f"https://en.wikipedia.org/w/index.php?title={wikipedia_page_name}&oldid={rev_id}"

    return url


@tool
def get_wikipedia_section_tables(
    section_name: str, soup_object: BeautifulSoup
) -> list[pd.DataFrame]:
    """
    A tool that extracts tables from a specific section of a Wikipedia page using BeautifulSoup and pandas.

    This function searches for a section in the following order:
    1. First tries to find an element with ID matching the section name
    2. If not found, tries to find an h2 element with text matching the section name
    3. If not found, tries to find an h3 element with text matching the section name

    Once the section is found, it goes to the parent element, finds the next <table> sibling,
    and uses pandas read_html to extract the table data.

    Args:
        section_name (str): The name of the section to extract table from
        soup_object: A BeautifulSoup object containing the parsed HTML content

    Returns:
        list: A list of pandas DataFrames representing tables found after the section,
              or empty list if no tables found

    Example:
        >>> from bs4 import BeautifulSoup
        >>> html = "<html><body><h2>Statistics</h2><table><tr><td>Data</td></tr></table></body></html>"
        >>> soup = BeautifulSoup(html, 'html.parser')
        >>> tables = get_wikipedia_section_table("Statistics", soup)
        >>> print(tables[0] if tables else "No tables found")
    """
    import pandas as pd
    from bs4 import BeautifulSoup

    if not soup_object:
        return []

    # Ensure we have a BeautifulSoup object
    if not isinstance(soup_object, BeautifulSoup):
        return []

    section_element = None

    # Strategy 1: Try to find element with ID same as section name
    # Convert section name to potential ID format (replace spaces with underscores, etc.)
    section_id = section_name.replace(" ", "_")
    element = soup_object.find(id=section_id)
    if element:
        section_element = element

    # Strategy 2: Try to find h2 element with text same as section name
    if not section_element:
        h2_elements = soup_object.find_all("h2")
        for h2 in h2_elements:
            if h2.get_text().strip() == section_name:
                section_element = h2
                break

    # Strategy 3: Try to find h3 element with text same as section name
    if not section_element:
        h3_elements = soup_object.find_all("h3")
        for h3 in h3_elements:
            if h3.get_text().strip() == section_name:
                section_element = h3
                break

    # If no section found, return empty list
    if not section_element:
        return []

    # Go to parent element and find next table sibling
    parent = section_element.parent
    if not parent:
        return []

    # Find the next table sibling from the parent
    table = parent.find_next_sibling("table")
    if not table:
        return []
    try:
        # Use pandas read_html to extract table data
        table_html = str(table)
        tables = pd.read_html(table_html)
        return tables if tables else []
    except ValueError:
        # No tables found or parsing error
        return []
    except Exception:
        # Any other error
        return []


@tool
def download_file(question_id: str, file_name: str) -> str:
    """
    A tool that downloads file that was mentioned in a question and store it as local file.
    Returns a JSON string containing the file path and optionally the text content if the file has a text MIME type.

    Args:
        question_id: Question ID.
        file_name: File name.
    Returns:
        str: JSON string containing file information. Structure:
             - For text files: {"path": "local_path", "content": "file_content"}
             - For non-text files: {"path": "local_path"}
    """
    import json
    import os

    import requests

    url = f"{DEFAULT_API_URL}/files/{question_id}"
    print(f"Fetching file from URL: {url}")

    # Create downloads directory if it doesn't exist
    response = None
    try:
        response = requests.get(url, timeout=30)
        response.raise_for_status()  # Raises an HTTPError for bad responses

        # Check if response is empty
        if not response.content:
            raise ValueError(f"Empty response received from {url}")

        # Check content type
        content_type = response.headers.get("content-type", "").lower()
        print(f"Response content-type: {content_type}")
        print(f"Response content length: {len(response.content)} bytes")

        # Use original filename directly
        local_path = file_name

        # Save the file locally
        with open(local_path, "wb") as f:
            f.write(response.content)

        print(f"File saved to: {local_path}")

        # Check if the file has a text MIME type
        text_mime_types = [
            "text/",
            "application/json",
            "application/xml",
            "application/javascript",
            "application/csv",
            "application/x-csv",
            "text/csv",
        ]

        is_text_file = any(
            content_type.startswith(mime_type) for mime_type in text_mime_types
        )

        result = {"path": local_path}

        if is_text_file:
            # Decode response content directly as text using UTF-8
            text_content = response.content.decode("utf-8")
            result["content"] = text_content
            print(
                f"Added text content to result (length: {len(text_content)} characters)"
            )

        return json.dumps(result)

    except requests.exceptions.RequestException as e:
        raise ValueError(f"Failed to download file from {url}: {e}")
    except Exception as e:
        # Print first 200 characters of response content for debugging
        content_preview = (
            response.content[:200]
            if response and hasattr(response, "content")
            else b"No response"
        )
        print(f"Error downloading file. Content preview: {content_preview}")
        raise ValueError(f"Failed to download file from {url}: {e}")


# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        self.multimodal_agent = CodeAgent(
            tools=[
                VisitWebpageTool(),
                GoogleSearchTool("serper"),
                download_file,
                audio_to_text,
                WikipediaSearchTool(),
                get_wikipedia_page_url_by_year,
                get_wikipedia_section_tables,
            ],
            model=OpenAIServerModel(model_id="gpt-4o"),
            additional_authorized_imports=[
                "requests",
                "bs4",
                "markdownify",
                "wikipedia",
                "pandas",
                "io",
                "PIL",
                "img2text",
                "PIL.Image",
                "cv2",
                "numpy",
                "whisper",
                "openpyxl",
                "json",
                "wikipediaapi",
                "pytube",
                "pytubefix",
                "pytubefix.cli",
                "youtube_transcript_api",
            ],
            name="multimodal_agent",
            description="""
                 This is a powerful agent, it specializes in: 
                    - Writing code to solve problem. 
                    - Solving hard Maths problems. 
                    - Browse the web to find information.
                    - Reason across audio, vision, and text, a.k.a multimodal agent. """,
            max_steps=5,
        )

        self.manager_agent = CodeAgent(
            model=InferenceClientModel(
                model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
            ),
            tools=[
                download_file,
                audio_to_text,
                get_wikipedia_page_url_by_year,
                get_wikipedia_section_tables,
            ],
            managed_agents=[self.multimodal_agent],
            additional_authorized_imports=[
                "requests",
                "bs4",
                "markdownify",
                "wikipedia",
                "io",
                "pandas",
                "PIL",
                "img2text",
                "PIL.Image",
                "cv2",
                "numpy",
                "openpyxl",
                "json",
                "wikipediaapi",
                "pytube",
                "pytubefix",
                "pytubefix.cli",
                "youtube_transcript_api",
            ],
            planning_interval=2,
            max_steps=10,
        )

    def __call__(self, question: str, question_id: str, file_name: str) -> str:
        print(f"Agent received question: {question}")
        file = f"Provided data file: {file_name}" if file_name else ""
        metadata = {}
        metadata["question_id"] = question_id
        if file_name:
            metadata["file_name"] = file_name

        prompt = f"""
            Answer the following question: 
               "{question}". 
            Question metadata in JSON format: 
            ```
            {json.dumps(metadata)}
            ```
            Follow below rules when possible:
                - Please take the question literally! Do not add any additional information or assumptions.
                - Please answer as concisely as possible.
                - If the question asks for a number, please return a numerical answer without unit (unless unit is specifically asked for). For example: 3 instead of three, 0 instead of None, 3 instead of $3.
                - If the question asks for a number with specific decimal places, please format the number into string with the same decimal places. For example: 3.00 instead of 3.
                - If the question asks for a list, please make sure that the elements are separated by a comma(`,`) and a space(` `). For example: `1, 2, 3` instead of `1,2,3`.
                - If the question asks for name without abbreviations, please ALWAYS ask `multimodal_agent` for the FULL name of final answer to ensure NO abbreviation is included in Final Answer. For example: `United States` instead of `US`.
                - To parse data from Wikipedia page, please use `get_wikipedia_section_tables` tool. 
        """
        if "food" in question.lower() or "drink" in question.lower():
            prompt = f"""
            {prompt}
               - Be careful about the difference between food and drink items. For instance: Ice Cream is a food item!
            """
        result = self.manager_agent.run(prompt)
        print(f"Agent responded with: {result}")
        return result


def run_and_submit_all(question_id: str, 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

    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}")
    response = None
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if question_id:
            questions_data = [
                item for item in questions_data if item.get("task_id") == question_id
            ]
        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.JSONDecodeError as e:
        print(f"Error decoding JSON response from questions endpoint: {e}")
        print(f"Response: {response}")
        return f"Error decoding server response for questions: {e}", None
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching 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:
        print(f"Question data: {json.dumps(item)}")
        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, task_id, 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:
        print(f"Submission_data: {submission_data}")
        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(f"Submission successful. Final status: {final_status}")
        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()

    question_id = gr.Textbox(
        label="Question id to solve (empty to solve all)",
        lines=1,
        interactive=True,
        value="7bd855d8-463d-4ed5-93ca-5fe35145f733",
    )
    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,
        inputs=[question_id],
        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)