app.py

import inspect
import json
import os
from io import BytesIO

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

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


@tool 
def extract_table_from_html(html: str) -> 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.
    
    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
        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_file(question_id: str, file_name: str) -> str:
    """
    A tool that downloads a file that was mentioned in a question.
    Args:
        question_id: Question ID.
        file_name: File name.
    Returns:
        str: Local file path where the text was saved.
    """
    import requests
    import os
    
    url = f"{DEFAULT_API_URL}/files/{question_id}"
    print(f"Fetching text file from URL: {url}")
    
    # Create downloads directory if it doesn't exist
    downloads_dir = "downloaded_texts"
    os.makedirs(downloads_dir, exist_ok=True)
    
    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 = os.path.join(downloads_dir, file_name)
        
        # Save the text file locally
        with open(local_path, 'wb') as f:
            f.write(response.content)
        
        print(f"Text file saved to: {local_path}")
        return local_path
        
    except requests.exceptions.RequestException as e:
        raise ValueError(f"Failed to fetch text 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 text file. Content preview: {content_preview}")
        raise ValueError(f"Failed to download text 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"), get_file, audio_to_text],
            model= OpenAIServerModel(model_id="gpt-4o"),
            additional_authorized_imports=[
                "requests",
                "bs4",
                "pandas",
                "io",
                "PIL",
                "chess",
                "img2text",
                "PIL.Image",
                "bytes",
                "cv2",
                "numpy",
                "json",
                "whisper",
                "openpyxl",
                "youtube-transcript-api",
            ],
            name="multimodal_agent",
            description="""
                This agent can reason across audio, vision, and text, a.k.a multimodal agent. """,
            verbosity_level=0,
            max_steps=10,
        ) 
        
        self.code_agent = CodeAgent(
            tools=[VisitWebpageTool(), GoogleSearchTool("serper"), get_file, audio_to_text, extract_table_from_html],
            model=InferenceClientModel(
                model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
            ),
            additional_authorized_imports=[
                "requests",
                "bs4",
                "markdownify",
                "wikipedia",
                "pandas",
                "io",
                "PIL",
                "chess",
                "img2text",
                "chess.pgn",
                "PIL.Image",
                "bytes",
                "cv2",
                "numpy",
                "chess.engine",
                "json",
                "whisper",
                "openpyxl",
                "youtube-transcript-api",
            ],
            name="code_agent",
            description="""
                This agent specializes at: 
                    - Writing code to solve problem. 
                    - Browse and search the web to find information.
                    - Solving chess problems. 
                    - Parsing Wikipedia pages.
                This agent follows rules below:
                1. Take the question literally! Do not add any additional information or assumptions.
                2. `wikipedia` Python library is provided that makes it easy to to interact with Wikipedia pages.
                3. `extract_table_from_html` tool is provided that makes it easy to extract tables from Wikipedia HTML pages.
            """,
            verbosity_level=0,
            max_steps=10,
        )

        self.manager_agent = CodeAgent(
            model=InferenceClientModel(
                "Qwen/Qwen2.5-32B-Instruct"
            ),
            tools=[GoogleSearchTool("serper"), extract_table_from_html],
            managed_agents=[
                self.multimodal_agent, 
                self.code_agent],
            additional_authorized_imports=[
                "requests",
                "bs4",
                "markdownify",
                "wikipedia",
                "pandas",
                "io",
                "PIL",
                "chess",
                "img2text",
                "chess.pgn",
                "PIL.Image",
                "bytes",
                "cv2",
                "numpy",
                "chess.engine",
                "whisper",
                "json",
                "youtube-transcript-api",
                "openpyxl",
            ],
            planning_interval=5,
            max_steps=15,
        )

    def __call__(self, question: str, question_id: str, file_name: str) -> str:
        print(f"Agent received question: {question}")
        file = f"Mentioned file: {file_name}" if file_name else ""
        prompt = f"""
            Answer the following question (question_id is {question_id}):): 
               "{question}""{file}"
            Please follow rules below:
                1. Take the question literally! Do not add any additional information or assumptions.
        """
        result = self.manager_agent.run(prompt)
        print(f"Agent responded with: {result}")
        return result


def run_and_submit_all(questions_index: 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
    QUESTION_INDEX = int(questions_index)

    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()[QUESTION_INDEX]]
            if QUESTION_INDEX >= 0
            else 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.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:
        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()

    questions_limit = gr.Textbox(
        label="Question index to solve (-1 to solve all)",
        lines=1,
        interactive=True,
        value="0",
    )
    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=[questions_limit],
        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)