app.py

import os
from dotenv import load_dotenv
import requests
import pandas as pd
import base64
import mimetypes
import tempfile
from smolagents import CodeAgent, OpenAIServerModel, tool
from dotenv import load_dotenv
from openai import OpenAI

# Load environment variables
load_dotenv()

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

# Initialize the OpenAI model using environment variable for API key
model = OpenAIServerModel(
    model_id="o4-mini-2025-04-16",
    api_base="https://api.openai.com/v1",
    api_key=os.getenv("openai"),
)

# Initialize OpenAI client
openAiClient = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

@tool
def tavily_search(query: str) -> str:
    """
    Perform a search using the Tavily API.
    
    Args:
        query: The search query string
        
    Returns:
        A string containing the search results
    """
    api_key = os.getenv("TAVILY_API_KEY")
    if not api_key:
        return "Error: TAVILY_API_KEY environment variable is not set"
    
    api_url = "https://api.tavily.com/search"
    
    headers = {
        "Content-Type": "application/json",
    }
    
    payload = {
        "api_key": api_key,
        "query": query,
        "search_depth": "advanced",
        "include_answer": True,
        "include_raw_content": False,
        "max_results": 5
    }
    
    try:
        response = requests.post(api_url, headers=headers, json=payload)
        response.raise_for_status()
        data = response.json()
        
        # Extract the answer and results
        result = []
        if "answer" in data:
            result.append(f"Answer: {data['answer']}")
        
        if "results" in data:
            result.append("\nSources:")
            for i, item in enumerate(data["results"], 1):
                result.append(f"{i}. {item.get('title', 'No title')}: {item.get('url', 'No URL')}")
        
        return "\n".join(result)
    except Exception as e:
        return f"Error performing Tavily search: {str(e)}"

@tool
def analyze_image(image_url: str) -> str:
    """
    Analyze an image using OpenAI's vision model and return a description.
    
    Args:
        image_url: URL of the image to analyze
        
    Returns:
        A detailed description of the image
    """
    api_key = os.getenv("OPENAI_API_KEY")
    if not api_key:
        return "Error: OpenAI API key not set in environment variables"
    
    # Download the image
    try:
        response = requests.get(image_url)
        response.raise_for_status()
        image_data = response.content
        base64_image = base64.b64encode(image_data).decode('utf-8')
    except Exception as e:
        return f"Error downloading image: {str(e)}"
    
    # Call OpenAI API
    api_url = "https://api.openai.com/v1/chat/completions"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }
    
    payload = {
        "model": "gpt-4.1-2025-04-14",
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "Describe this image in detail. Include any text, objects, people, actions, and overall context."
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}"
                        }
                    }
                ]
            }
        ],
        "max_tokens": 500
    }
    
    try:
        response = requests.post(api_url, headers=headers, json=payload)
        response.raise_for_status()
        data = response.json()
        
        if "choices" in data and len(data["choices"]) > 0:
            return data["choices"][0]["message"]["content"]
        else:
            return "No description generated"
    except Exception as e:
        return f"Error analyzing image: {str(e)}"

@tool
def analyze_sound(audio_url: str) -> str:
    """
    Transcribe an audio file using OpenAI's Whisper model.
    
    Args:
        audio_url: the url of the audio
        
    Returns:
        A transcription of the audio content
    """
    api_key = os.getenv("OPENAI_API_KEY")
    if not api_key:
        return "Error: OpenAI API key not set in environment variables"
    
    # Download the audio file
    try:
        response = requests.get(audio_url)
        response.raise_for_status()
        
        import tempfile
        with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file:
            temp_file.write(response.content)
            temp_file_path = temp_file.name

        audio_file= open(temp_file_path, "rb")
        
    except Exception as e:
        return f"Error downloading audio: {str(e)}"

    try:
        transcription = openAiClient.audio.transcriptions.create(
            model="gpt-4o-transcribe", 
            file=audio_file
        )
        return transcription.text
    except Exception as e:
        return f"Error transcribing audio: {str(e)}"

@tool
def analyze_excel(excel_url: str) -> str:
    """
    Process an Excel file and convert it to a text-based format.
    
    Args:
        excel_url: URL of the Excel file to analyze
        
    Returns:
        A text representation of the Excel data
    """
    try:
        # Download the Excel file
        response = requests.get(excel_url)
        response.raise_for_status()
        
        # Save to a temporary file
        with tempfile.NamedTemporaryFile(suffix=".xlsx", delete=False) as temp_file:
            temp_file.write(response.content)
            temp_file_path = temp_file.name
        
        # Read the Excel file
        df = pd.read_excel(temp_file_path)
        
        # Convert to a text representation
        result = []
        
        # Add sheet information
        result.append(f"Excel file with {len(df)} rows and {len(df.columns)} columns")
        
        # Add column names
        result.append("\nColumns:")
        for i, col in enumerate(df.columns, 1):
            result.append(f"{i}. {col}")
        
        # Add data summary
        result.append("\nData Summary:")
        result.append(df.describe().to_string())
        
        # Add first few rows as a sample
        result.append("\nFirst 5 rows:")
        result.append(df.head().to_string())
        
        # Clean up
        os.unlink(temp_file_path)
        
        return "\n".join(result)
    except Exception as e:
        return f"Error processing Excel file: {str(e)}"

@tool
def analyze_text(text_url: str) -> str:
    """
    Process a text file and return its contents.
    
    Args:
        text_url: URL of the text file to analyze
        
    Returns:
        The contents of the text file
    """
    try:
        # Download the text file
        response = requests.get(text_url)
        response.raise_for_status()
        
        # Get the text content
        text_content = response.text
        
        # For very long files, truncate with a note
        if len(text_content) > 10000:
            return f"Text file content (truncated to first 10000 characters):\n\n{text_content[:10000]}\n\n... [content truncated]"
        
        return f"Text file content:\n\n{text_content}"
    except Exception as e:
        return f"Error processing text file: {str(e)}"

@tool
def transcribe_youtube(youtube_url: str) -> str:
    """
    Extract the transcript from a YouTube video.
    
    Args:
        youtube_url: URL of the YouTube video
        
    Returns:
        The transcript of the video
    """
    try:
        # Extract video ID from URL
        import re
        video_id_match = re.search(r'(?:v=|\/)([0-9A-Za-z_-]{11}).*', youtube_url)
        if not video_id_match:
            return "Error: Invalid YouTube URL"
        
        video_id = video_id_match.group(1)
        
        # Use youtube_transcript_api to get the transcript
        from youtube_transcript_api import YouTubeTranscriptApi
        
        try:
            transcript_list = YouTubeTranscriptApi.get_transcript(video_id)
            
            # Combine all transcript segments into a single text
            full_transcript = ""
            for segment in transcript_list:
                full_transcript += segment['text'] + " "
            
            return f"YouTube Video Transcript:\n\n{full_transcript.strip()}"
        except Exception as e:
            return f"Error extracting transcript: {str(e)}"
    except Exception as e:
        return f"Error processing YouTube video: {str(e)}"

"""
@tool
def process_file(task_id: str, file_name: str) -> str:
    """
    Fetch and process a file based on task_id and file_name.
    For images, it will analyze them and return a description of the image.
    For audio files, it will transcribe them.
    For Excel files, it will convert them to a text format.
    For text files, it will return the file contents.
    Other file types can be ignored for this tool.
    
    Args:
        task_id: The task ID to fetch the file for
        file_name: The name of the file to process
        
    Returns:
        A description or transcription of the file content
    """
    if not task_id or not file_name:
        return "Error: task_id and file_name are required"
    
    # Construct the file URL
    file_url = f"{DEFAULT_API_URL}/files/{task_id}"
    
    try:
        # Fetch the file
        response = requests.get(file_url)
        response.raise_for_status()
        
        # Determine file type
        mime_type, _ = mimetypes.guess_type(file_name)
        
        # Process based on file type
        if mime_type and mime_type.startswith('image/'):
            # For images, use the analyze_image tool
            return analyze_image(file_url)
        elif file_name.lower().endswith('.mp3') or (mime_type and mime_type.startswith('audio/')):
            # For audio files, use the analyze_sound tool
            return analyze_sound(file_url)
        elif file_name.lower().endswith('.xlsx') or (mime_type and mime_type == 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'):
            # For Excel files, use the analyze_excel tool
            return analyze_excel(file_url)
        elif file_name.lower().endswith(('.txt', '.py', '.js', '.html', '.css', '.json', '.md')) or (mime_type and mime_type.startswith('text/')):
            # For text files, use the analyze_text tool
            return analyze_text(file_url)
        else:
            # For other file types, return basic information
            return f"File '{file_name}' of type '{mime_type or 'unknown'}' was fetched successfully. Content processing not implemented for this file type."
    except Exception as e:
        return f"Error processing file: {str(e)}"
"""

class BasicAgent:
    """
    A simple agent that uses smolagents.CodeAgent with multiple specialized tools:
    - Tavily search tool for web searches
    - Image analysis tool for processing images
    - Audio transcription tool for processing sound files
    - Excel analysis tool for processing spreadsheet data
    - Text file analysis tool for processing code and text files
    - YouTube transcription tool for processing video content
    - File processing tool for handling various file types
    
    The CodeAgent is instantiated once and reused for each question to reduce overhead.
    """
    def __init__(self):
        print("BasicAgent initialized.")
        # Reuse a single CodeAgent instance for all queries
        self.agent = CodeAgent(tools=[tavily_search, analyze_image, analyze_sound, analyze_excel, analyze_text, transcribe_youtube, process_file], model=model)

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        return self.agent.run(question)



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

    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)
            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)