app.py

import os
import tempfile
import urllib.parse
from pathlib import Path
import subprocess
import gradio as gr
import requests
import inspect
import pandas as pd
from smolagents import CodeAgent, PythonInterpreterTool, WebSearchTool, VisitWebpageTool, WikipediaSearchTool, InferenceClientModel, tool
import numpy as np

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

# --- File Download Tools ---


@tool
def download_youtube_video(video_url: str) -> str:
    """
    Downloads a YouTube video using yt-dlp.

    Args:
        video_url: The YouTube URL to download

    Returns:
        str: Path to the downloaded video file or error message
    """
    try:
        temp_dir = tempfile.mkdtemp()
        output_path = Path(temp_dir) / "video.%(ext)s"

        # Use yt-dlp to download the video
        cmd = [
            "yt-dlp",
            # Limit quality to reduce file size
            "--format", "best[height<=720]",
            "--output", str(output_path),
            video_url
        ]

        result = subprocess.run(
            cmd, capture_output=True, text=True, timeout=300)

        if result.returncode != 0:
            return f"Error downloading video: {result.stderr}"

        # Find the downloaded file
        video_files = list(Path(temp_dir).glob("video.*"))
        if video_files:
            return str(video_files[0])
        else:
            return "Error: Video file not found after download"

    except subprocess.TimeoutExpired:
        return "Error: Video download timed out"
    except Exception as e:
        return f"Error downloading YouTube video: {e}"


@tool
def extract_video_frames(video_path: str, max_frames: int = 30) -> str:
    """
    Extracts frames from a video file.

    Args:
        video_path: Path to the video file
        max_frames: Maximum number of frames to extract

    Returns:
        str: Directory path containing extracted frames or error message
    """
    try:
        try:
            import cv2
        except ImportError:
            return "Error: OpenCV (cv2) not available for video processing"

        if not Path(video_path).exists():
            return f"Video file not found: {video_path}"

        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            return f"Error: Cannot open video file {video_path}"

        # Create directory for frames
        frames_dir = Path(video_path).parent / "frames"
        frames_dir.mkdir(exist_ok=True)

        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        fps = cap.get(cv2.CAP_PROP_FPS)

        # Calculate frame interval to extract evenly distributed frames
        frame_interval = max(1, total_frames // max_frames)

        frame_count = 0
        extracted_count = 0

        while cap.isOpened() and extracted_count < max_frames:
            ret, frame = cap.read()
            if not ret:
                break

            if frame_count % frame_interval == 0:
                frame_path = frames_dir / f"frame_{extracted_count:04d}.jpg"
                cv2.imwrite(str(frame_path), frame)
                extracted_count += 1

            frame_count += 1

        cap.release()

        return f"Extracted {extracted_count} frames to {frames_dir}"

    except ImportError:
        return "Error: OpenCV (cv2) not available for video processing"
    except Exception as e:
        return f"Error extracting video frames: {e}"


@tool
def analyze_image_with_description(image_path: str) -> str:
    """
    Analyzes an image and provides a detailed description.

    Args:
        image_path: Path to the image file

    Returns:
        str: Image analysis description or error message
    """
    try:
        from PIL import Image

        if not Path(image_path).exists():
            return f"Image file not found: {image_path}"

        # Open and analyze the image
        img = Image.open(image_path)

        # Get basic image info
        width, height = img.size
        mode = img.mode
        format_info = img.format or "Unknown"

        # Convert to RGB if needed for analysis
        if img.mode != 'RGB':
            img_rgb = img.convert('RGB')
        else:
            img_rgb = img

        # Basic color analysis
        pixels = np.array(img_rgb)
        avg_color = np.mean(pixels, axis=(0, 1))

        analysis = {
            "file_path": image_path,
            "dimensions": f"{width}x{height}",
            "mode": mode,
            "format": format_info,
            "average_color_rgb": avg_color.tolist(),
            "file_size_bytes": Path(image_path).stat().st_size
        }

        return str(analysis)

    except ImportError:
        return "Error: PIL not available for image processing"
    except Exception as e:
        return f"Error analyzing image: {e}"


@tool
def process_chess_image(image_path: str) -> str:
    """
    Processes a chess board image to identify piece positions.

    Args:
        image_path: Path to the chess board image

    Returns:
        str: Chess position description or error message
    """
    try:
        from PIL import Image

        if not Path(image_path).exists():
            return f"Chess image file not found: {image_path}"

        img = Image.open(image_path)

        # Basic analysis of the chess image
        width, height = img.size

        analysis = {
            "image_path": image_path,
            "dimensions": f"{width}x{height}",
            "analysis_note": "Chess position analysis requires specialized computer vision models. This tool provides basic image information. For detailed piece recognition, the vision agent should analyze the image directly using its vision capabilities.",
            "recommendation": "Pass this image directly to the vision agent for detailed chess position analysis."
        }

        return str(analysis)

    except Exception as e:
        return f"Error processing chess image: {e}"


@tool
def download_task_file(task_id: str) -> str:
    """
    Downloads a file associated with a specific task ID from the evaluation system.

    Args:
        task_id: The task ID to download files for

    Returns:
        str: Path to the downloaded file or error message
    """
    try:
        file_url = f"{DEFAULT_API_URL}/files/{task_id}"
        response = requests.get(file_url, timeout=30)
        response.raise_for_status()

        # Create a temporary directory for downloaded files
        temp_dir = tempfile.mkdtemp()

        # Try to get filename from Content-Disposition header
        filename = f"task_{task_id}_file"
        if 'content-disposition' in response.headers:
            content_disposition = response.headers['content-disposition']
            if 'filename=' in content_disposition:
                filename = content_disposition.split('filename=')[1].strip('"')

        # Save the file
        file_path = Path(temp_dir) / filename
        with open(file_path, 'wb') as f:
            f.write(response.content)

        return str(file_path)
    except requests.exceptions.RequestException as e:
        return f"Error downloading task file: {e}"
    except Exception as e:
        return f"Error saving task file: {e}"


@tool
def download_file_from_url(url: str, filename: str = "") -> str:
    """
    Downloads a file from any given URL.

    Args:
        url: The URL to download the file from
        filename: Optional filename to save as (if not provided, will extract from URL)

    Returns:
        str: Path to the downloaded file or error message
    """
    try:
        response = requests.get(url, timeout=30)
        response.raise_for_status()

        # Create a temporary directory for downloaded files
        temp_dir = tempfile.mkdtemp()

        # Determine filename
        if not filename:
            parsed_url = urllib.parse.urlparse(url)
            filename = Path(parsed_url.path).name
            if not filename:
                filename = "downloaded_file"

        # Save the file
        file_path = Path(temp_dir) / filename
        with open(file_path, 'wb') as f:
            f.write(response.content)

        return str(file_path)
    except requests.exceptions.RequestException as e:
        return f"Error downloading file from URL: {e}"
    except Exception as e:
        return f"Error saving downloaded file: {e}"


@tool
def read_downloaded_file(file_path: str, encoding: str = "utf-8") -> str:
    """
    Reads the content of a downloaded file.

    Args:
        file_path: Path to the file to read
        encoding: Text encoding to use (default: utf-8)

    Returns:
        str: File content or error message
    """
    try:
        file_path_obj = Path(file_path)
        if not file_path_obj.exists():
            return f"File not found: {file_path}"

        # Try to read as text first
        try:
            with open(file_path_obj, 'r', encoding=encoding) as f:
                content = f.read()
            return content
        except UnicodeDecodeError:
            # If text reading fails, provide file info
            file_size = file_path_obj.stat().st_size
            file_ext = file_path_obj.suffix
            return f"Binary file detected. File: {file_path}, Size: {file_size} bytes, Extension: {file_ext}"
    except Exception as e:
        return f"Error reading file: {e}"


@tool
def list_file_info(file_path: str) -> str:
    """
    Gets information about a downloaded file.

    Args:
        file_path: Path to the file

    Returns:
        str: File information including size, type, etc.
    """
    try:
        file_path_obj = Path(file_path)
        if not file_path_obj.exists():
            return f"File not found: {file_path}"

        stat = file_path_obj.stat()
        info = {
            "name": file_path_obj.name,
            "path": str(file_path_obj),
            "size_bytes": stat.st_size,
            "size_kb": round(stat.st_size / 1024, 2),
            "extension": file_path_obj.suffix,
            "is_text": file_path_obj.suffix.lower() in ['.txt', '.md', '.json', '.xml', '.csv', '.py', '.js', '.html', '.css']
        }

        return str(info)
    except Exception as e:
        return f"Error getting file info: {e}"


# --- Visual Agent ---
visual_model = InferenceClientModel(
    model_id="Qwen/Qwen2.5-VL-72B-Instruct", provider="nebius", temperature=0
)

# --- Multi-Agent System Definition ---


class MultiAgentSystem:
    def __init__(self):
        # Initialize models
        text_model = InferenceClientModel(
            model_id="Qwen/Qwen3-235B-A22B", provider="nebius", temperature=0
        )

        # Create Vision Agent with enhanced capabilities
        self.vision_agent = CodeAgent(
            model=visual_model,
            tools=[
                PythonInterpreterTool(),
                download_task_file,
                download_file_from_url,
                download_youtube_video,
                extract_video_frames,
                analyze_image_with_description,
                process_chess_image,
                read_downloaded_file,
                list_file_info
            ],
            additional_authorized_imports=[
                "requests",
                "json",
                "pandas",
                "numpy",
                "PIL",
                "PIL.Image",
                "cv2",
                "base64",
                "io",
                "tempfile",
                "pathlib",
                "subprocess",
                "re",
                "os",
                "posixpath"
            ],
            max_steps=15,
            name="vision_agent",
            description="Processes images, videos, downloads files, analyzes visual content including chess positions and video frames. IMPORTANT: Answer should be a number OR as few words as possible OR a comma separated list. For numbers, don't use commas or units unless specified. For strings, don't use articles or abbreviations.",
            verbosity_level=0
        )

        # Create Web Search Agent with enhanced capabilities
        self.web_agent = CodeAgent(
            model=text_model,
            tools=[
                PythonInterpreterTool(),
                WebSearchTool(),
                VisitWebpageTool(),
                WikipediaSearchTool(),
                download_task_file,
                download_file_from_url,
                download_youtube_video,
                read_downloaded_file,
                list_file_info
            ],
            additional_authorized_imports=[
                "requests",
                "json",
                "pandas",
                "numpy",
                "re",
                "time",
                "datetime",
                "tempfile",
                "pathlib",
                "subprocess",
                "os",
                "posixpath"
            ],
            max_steps=15,
            name="web_agent",
            description="Searches web, downloads files and videos, gathers information from online sources. IMPORTANT: Answer should be a number OR as few words as possible OR a comma separated list. For numbers, don't use commas or units unless specified. For strings, don't use articles or abbreviations.",
            verbosity_level=0
        )

        # Create Manager Agent to coordinate the other agents
        self.manager_agent = CodeAgent(
            model=text_model,
            tools=[
                PythonInterpreterTool(),
                download_task_file,
                download_file_from_url,
                read_downloaded_file,
                list_file_info
            ],
            managed_agents=[self.vision_agent, self.web_agent],
            additional_authorized_imports=[
                "requests",
                "json",
                "pandas",
                "numpy",
                "re",
                "time",
                "datetime",
                "os",
                "inspect",
                "random",
                "math",
                "tempfile",
                "pathlib",
                "posixpath"
            ],
            max_steps=25,
            name="manager_agent",
            description="Coordinates vision and web agents to solve complex tasks involving videos, images, and web research. IMPORTANT: Answer should be a number OR as few words as possible OR a comma separated list. For numbers, don't use commas or units unless specified. For strings, don't use articles or abbreviations.",
            verbosity_level=1
        )

        print("Multi-Agent System initialized with enhanced Vision Agent, Web Agent, and Manager Agent.")

    def run(self, question: str, images=None, task_id=None) -> str:
        """
        Run the multi-agent system on a question.
        If images are provided, they will be available to the vision agent.
        If task_id is provided, agents can download task-specific files.
        """
        try:
            # Add formatting instructions to the question
            enhanced_question = question + "\n\nIMPORTANT: Answer should be a number OR as few words as possible OR a comma separated list. For numbers, don't use commas or units unless specified. For strings, don't use articles or abbreviations."

            if task_id:
                enhanced_question += f"\n\nTask ID: {task_id} (use download_task_file tool if files are needed)"

            # If images are provided, use the vision agent directly for image-related tasks
            if images:
                result = self.vision_agent.run(
                    enhanced_question, images=images)
            else:
                # Use the manager agent to coordinate between vision and web agents
                result = self.manager_agent.run(enhanced_question)

            return str(result)
        except Exception as e:
            return f"Error in multi-agent system: {str(e)}"

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------


class BasicAgent:
    def __init__(self):
        # Initialize the multi-agent system
        self.multi_agent_system = MultiAgentSystem()
        print("BasicAgent initialized with Enhanced Multi-Agent System.")

    def __call__(self, question: str, task_id: str = "") -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        try:
            # Use the multi-agent system to process the question with task_id
            answer = self.multi_agent_system.run(question, task_id=task_id)
        except Exception as e:
            print(f"Error running multi-agent system: {e}")
            answer = f"Error: {str(e)}"

        print(f"Agent returning answer: {answer}")
        return answer


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")

    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}")
    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, task_id)
            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)