app.py

import os
import gradio as gr
import requests
import pandas as pd
from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, tool
import re
import json
import math
import tempfile
from pathlib import Path
from urllib.parse import urlparse, parse_qs
import yt_dlp
from PIL import Image
import pytesseract

hf_token = os.getenv("HF_TOKEN")
SPACE_ID = os.getenv("SPACE_ID")
SPACE_HOST = os.getenv("SPACE_HOST")
# --- OUTILS CRITIQUES POUR GAIA ---
@tool
def web_browser(url: str) -> str:
    """
    Fetches content from a web URL.
    
    Args:
        url: The URL to fetch content from.
        
    Returns:
        Text content from the webpage.
    """
    try:
        headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
        }
        response = requests.get(url, headers=headers, timeout=10)
        response.raise_for_status()
        
        # Simple text extraction (you might want to use BeautifulSoup for better parsing)
        content = response.text
        # Basic cleaning
        content = re.sub(r'<[^>]+>', ' ', content)  # Remove HTML tags
        content = re.sub(r'\s+', ' ', content).strip()  # Clean whitespace
        
        return content[:2000] + "..." if len(content) > 2000 else content
        
    except Exception as e:
        return f"Error accessing URL: {str(e)}"

@tool
def youtube_transcript_extractor(url: str) -> str:
    """
    Extracts transcript or information from YouTube videos.
    
    Args:
        url: YouTube URL.
        
    Returns:
        Video information and transcript if available.
    """
    try:
        # Extract video ID from URL
        if "youtube.com/watch" in url:
            video_id = parse_qs(urlparse(url).query).get('v', [None])[0]
        elif "youtu.be/" in url:
            video_id = urlparse(url).path[1:]
        else:
            return "Invalid YouTube URL format"
            
        if not video_id:
            return "Could not extract video ID from URL"
        
        # Use youtube-dl to get video info
        ydl_opts = {
            'quiet': True,
            'no_warnings': True,
            'writesubtitles': True,
            'writeautomaticsub': True,
        }
        
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            info = ydl.extract_info(f"https://www.youtube.com/watch?v={video_id}", download=False)
            
            result = f"Title: {info.get('title', 'N/A')}\n"
            result += f"Description: {info.get('description', 'N/A')[:500]}...\n"
            result += f"Duration: {info.get('duration', 'N/A')} seconds\n"
            result += f"View count: {info.get('view_count', 'N/A')}\n"
            
            # Try to get subtitles/transcript
            if 'subtitles' in info and info['subtitles']:
                result += "\n--- Transcript Available ---\n"
                # This is a simplified approach - you'd need more complex logic for full transcript
                
            return result
            
    except Exception as e:
        return f"Error extracting YouTube content: {str(e)}"

@tool
def image_ocr_analyzer(image_path: str) -> str:
    """
    Performs OCR on images to extract text.
    
    Args:
        image_path: Path to the image file.
        
    Returns:
        Extracted text from the image.
    """
    try:
        # Open image with PIL
        image = Image.open(image_path)
        
        # Perform OCR
        extracted_text = pytesseract.image_to_string(image)
        
        if not extracted_text.strip():
            return "No text found in the image"
            
        return f"Extracted text:\n{extracted_text.strip()}"
        
    except Exception as e:
        return f"Error performing OCR: {str(e)}"

@tool
def pdf_text_extractor(file_path: str) -> str:
    """
    Extracts text from PDF files.
    
    Args:
        file_path: Path to the PDF file.
        
    Returns:
        Extracted text from PDF.
    """
    try:
        import PyPDF2
        
        with open(file_path, 'rb') as file:
            pdf_reader = PyPDF2.PdfReader(file)
            text = ""
            
            for page_num in range(len(pdf_reader.pages)):
                page = pdf_reader.pages[page_num]
                text += page.extract_text() + "\n"
                
        return text[:3000] + "..." if len(text) > 3000 else text
        
    except Exception as e:
        return f"Error extracting PDF text: {str(e)}"

@tool
def veterinary_document_analyzer(text: str) -> str:
    """
    Analyzes veterinary documents to extract specific information like names.
    
    Args:
        text: Document text to analyze.
        
    Returns:
        Extracted veterinary information.
    """
    try:
        # Look for veterinarian names and surnames
        vet_patterns = [
            r"Dr\.?\s+([A-Z][a-z]+)\s+([A-Z][a-z]+)",  # Dr. First Last
            r"Doctor\s+([A-Z][a-z]+)\s+([A-Z][a-z]+)",  # Doctor First Last
            r"veterinarian\s+([A-Z][a-z]+)\s+([A-Z][a-z]+)",  # veterinarian First Last
            r"DVM\s+([A-Z][a-z]+)\s+([A-Z][a-z]+)",  # DVM First Last
        ]
        
        found_vets = []
        for pattern in vet_patterns:
            matches = re.findall(pattern, text, re.IGNORECASE)
            for match in matches:
                full_name = f"{match[0]} {match[1]}"
                if full_name not in found_vets:
                    found_vets.append(full_name)
        
        if found_vets:
            return f"Found veterinarian(s): {', '.join(found_vets)}"
        else:
            return "No veterinarian names found in the document"
            
    except Exception as e:
        return f"Error analyzing veterinary document: {str(e)}"

# --- Outils existants améliorés ---
@tool
def analyze_excel_file(file_path: str, analysis_type: str = "general") -> str:
    """
    Analyzes Excel files with multiple analysis types.
    """
    try:
        df = pd.read_excel(file_path)

        if analysis_type == "general":
            return f"Excel file contains {len(df)} rows and {len(df.columns)} columns. Columns: {list(df.columns)}"

        elif analysis_type == "food_sales":
            if 'category' in df.columns and 'price' in df.columns and 'quantity' in df.columns:
                food_df = df[df['category'].str.lower() == 'food']
                total_sales = (food_df['price'] * food_df['quantity']).sum()
                return f"Total food sales: ${total_sales:.2f}"
            else:
                return "Required columns (category, price, quantity) not found"

        elif analysis_type == "summary":
            summary = df.describe(include='all').to_string()
            return f"Data summary:\n{summary}"

        elif analysis_type == "categories":
            if 'category' in df.columns:
                categories = df['category'].value_counts()
                return f"Categories breakdown:\n{categories.to_string()}"
            else:
                return "No category column found"

        return "Unknown analysis type"

    except Exception as e:
        return f"Error analyzing Excel file: {str(e)}"

@tool
def advanced_calculator(expression: str) -> str:
    """
    Evaluates mathematical expressions safely, including advanced functions.
    """
    try:
        expression = expression.replace('^', '**')
        allowed_functions = {
            'abs': abs, 'round': round, 'min': min, 'max': max,
            'sum': sum, 'len': len,
            'sqrt': math.sqrt, 'pow': math.pow, 'log': math.log,
            'sin': math.sin, 'cos': math.cos, 'tan': math.tan,
            'pi': math.pi, 'e': math.e,
            'floor': math.floor, 'ceil': math.ceil
        }
        result = eval(expression, {"__builtins__": {}}, allowed_functions)
        return str(result)

    except Exception as e:
        return f"Error in calculation: {str(e)}"

@tool
def smart_text_analyzer(text: str, task_type: str = "general") -> str:
    """
    Analyzes text with focus on GAIA-specific tasks.
    
    Args:
        text: Text to analyze.
        task_type: 'general', 'names', 'dates', 'numbers', 'veterinary'.
        
    Returns:
        Analysis results.
    """
    try:
        if task_type == "names":
            # Extract proper names
            name_pattern = r'\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*\b'
            names = re.findall(name_pattern, text)
            return f"Found names: {list(set(names))}"
            
        elif task_type == "veterinary":
            return veterinary_document_analyzer(text)
            
        elif task_type == "dates":
            date_patterns = [
                r'\d{1,2}/\d{1,2}/\d{4}',  # MM/DD/YYYY
                r'\d{4}-\d{2}-\d{2}',      # YYYY-MM-DD
                r'\b\w+\s+\d{1,2},\s+\d{4}\b'  # Month DD, YYYY
            ]
            dates = []
            for pattern in date_patterns:
                dates.extend(re.findall(pattern, text))
            return f"Found dates: {dates}"
            
        elif task_type == "numbers":
            numbers = re.findall(r'-?\d+\.?\d*', text)
            return f"Found numbers: {[float(n) for n in numbers if n]}"
            
        else:
            return f"Characters: {len(text)}, Words: {len(text.split())}, Lines: {len(text.splitlines())}"

    except Exception as e:
        return f"Error in text analysis: {str(e)}"

# --- Configuration du modèle OPTIMISÉE ---
# Changer pour un modèle plus léger qui ne dépasse pas ton quota
model = HfApiModel(
    max_tokens=2048,  # Réduit pour économiser le quota
    temperature=0.1,
    model_id='microsoft/DialoGPT-medium',  # Modèle plus léger
    # Ou essaye: 'HuggingFaceH4/zephyr-7b-beta' si disponible
)

# --- Initialisation des outils ---
search_tool = DuckDuckGoSearchTool()

# IMPORTANT: Ajouter TOUS les outils à la liste
tools = [
    search_tool,  # ⚠️ TU AVAIS OUBLIÉ ÇA !
    web_browser,
    youtube_transcript_extractor,
    image_ocr_analyzer,
    pdf_text_extractor,
    veterinary_document_analyzer,
    smart_text_analyzer,
    advanced_calculator,
    analyze_excel_file,
]

# Agent avec plus d'étapes pour les tâches complexes
agent_code = CodeAgent(
    tools=tools,
    model=model,
    max_steps=15,  # Augmenté pour les tâches complexes GAIA
    additional_authorized_imports=[
        "os", "tempfile", "pathlib", "re", "json", "math", "pandas",
        "requests", "PIL", "pytesseract", "PyPDF2", "yt_dlp"
    ]
)
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

class BasicAgent:
    def __init__(self):
        print("Enhanced GAIA Agent initialized with web browsing capabilities.")
        self.agent = agent_code

    def __call__(self, question: str) -> str:
        try:
            # Prompt amélioré spécifiquement pour GAIA
            enhanced_question = self._create_gaia_prompt(question)
            
            result = self.agent.run(enhanced_question)
            
            # Post-processing pour GAIA
            cleaned_result = self._clean_gaia_result(result)
            
            return cleaned_result if cleaned_result else "No response generated."
        
        except Exception as e:
            print(f"Agent error: {e}")
            # Fallback strategy
            try:
                fallback_prompt = f"""
                CRITICAL GAIA TASK: {question}
                
                Use available tools to find the answer. If it's a YouTube video, use youtube_transcript_extractor.
                If it's about documents, use appropriate analyzers.
                Be precise and direct in your final answer.
                """
                simple_result = self.agent.run(fallback_prompt)
                return simple_result if simple_result else f"Error: {e}"
            except:
                return f"Error: {e}"
    
    def _create_gaia_prompt(self, question: str) -> str:
        """Crée un prompt optimisé pour GAIA."""
        return f"""
        GAIA EVALUATION TASK - ANSWER PRECISELY
        
        Question: {question}
        
        INSTRUCTIONS:
        1. If this involves a YouTube video, use youtube_transcript_extractor tool
        2. If this involves web content, use web_browser tool  
        3. If this involves documents/PDFs, use appropriate analyzers
        4. If this involves images, use image_ocr_analyzer
        5. If this needs search, use the search tool
        6. For calculations, use advanced_calculator
        7. Be EXACT and SPECIFIC in your final answer
        8. Don't provide explanations unless asked - just the answer
        
        Work step by step and use the right tools for this task.
        """





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)