app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import re
import json
import urllib.parse
from bs4 import BeautifulSoup
import numpy as np
import sympy as sp
from datetime import datetime, timedelta
import dateutil.parser

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

# --- GAIA Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
class GaiaAgent:
    def __init__(self):
        print("GaiaAgent initialized.")
        self.session = requests.Session()
        self.session.headers.update({
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
        })
    
    def search_web(self, query, max_results=3):
        """Perform web search using DuckDuckGo instant answers or basic search"""
        try:
            # Try DuckDuckGo instant answer API first
            ddg_url = f"https://api.duckduckgo.com/?q={urllib.parse.quote(query)}&format=json&no_html=1&skip_disambig=1"
            response = self.session.get(ddg_url, timeout=10)
            if response.status_code == 200:
                data = response.json()
                if data.get('AbstractText'):
                    return data['AbstractText']
                if data.get('Answer'):
                    return data['Answer']
            
            # Fallback to basic web scraping (limited)
            search_url = f"https://html.duckduckgo.com/html/?q={urllib.parse.quote(query)}"
            response = self.session.get(search_url, timeout=10)
            if response.status_code == 200:
                soup = BeautifulSoup(response.text, 'html.parser')
                results = soup.find_all('a', class_='result__snippet', limit=max_results)
                if results:
                    return " ".join([r.get_text().strip() for r in results])
            
            return f"Unable to search for: {query}"
        except Exception as e:
            return f"Search error: {str(e)}"
    
    def calculate_math(self, expression):
        """Safely evaluate mathematical expressions"""
        try:
            # Clean the expression
            expression = re.sub(r'[^0-9+\-*/().\s]', '', expression)
            # Use sympy for safe evaluation
            result = sp.sympify(expression).evalf()
            return str(result)
        except Exception as e:
            return f"Math error: {str(e)}"
    
    def parse_date(self, date_string):
        """Parse various date formats"""
        try:
            parsed_date = dateutil.parser.parse(date_string)
            return parsed_date.strftime("%Y-%m-%d")
        except Exception as e:
            return f"Date parsing error: {str(e)}"
    
    def extract_numbers(self, text):
        """Extract numbers from text"""
        numbers = re.findall(r'-?\d+\.?\d*', text)
        return [float(n) for n in numbers if n]
    
    def process_question(self, question):
        """Process different types of questions with various strategies"""
        question_lower = question.lower()
        
        # Mathematical questions
        if any(word in question_lower for word in ['calculate', 'compute', 'math', '+', '-', '*', '/', 'equals', 'sum', 'product']):
            numbers = self.extract_numbers(question)
            if len(numbers) >= 2:
                if 'sum' in question_lower or '+' in question:
                    return str(sum(numbers))
                elif 'product' in question_lower or '*' in question:
                    result = 1
                    for n in numbers:
                        result *= n
                    return str(result)
                elif 'difference' in question_lower or '-' in question:
                    return str(numbers[0] - numbers[1] if len(numbers) >= 2 else numbers[0])
            
            # Try to extract and evaluate mathematical expressions
            math_pattern = r'[\d+\-*/().\s]+'
            math_expr = re.search(math_pattern, question)
            if math_expr:
                return self.calculate_math(math_expr.group())
        
        # Date/time questions
        if any(word in question_lower for word in ['date', 'time', 'year', 'month', 'day', 'when', 'ago', 'from now']):
            # Try to extract dates
            date_patterns = [
                r'\d{4}-\d{2}-\d{2}',
                r'\d{1,2}/\d{1,2}/\d{4}',
                r'\d{1,2}-\d{1,2}-\d{4}'
            ]
            for pattern in date_patterns:
                dates = re.findall(pattern, question)
                if dates:
                    return self.parse_date(dates[0])
            
            # If asking about current date/time
            if 'today' in question_lower or 'now' in question_lower:
                return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        
        # Questions that might need web search
        if any(word in question_lower for word in ['who is', 'what is', 'where is', 'when did', 'how many', 'capital of', 'population of']):
            search_result = self.search_web(question)
            if search_result and "error" not in search_result.lower():
                return search_result
        
        # Geography questions
        if any(word in question_lower for word in ['country', 'city', 'capital', 'continent', 'ocean', 'river']):
            search_result = self.search_web(question)
            if search_result and "error" not in search_result.lower():
                return search_result
        
        # Science/factual questions
        if any(word in question_lower for word in ['element', 'chemical', 'planet', 'temperature', 'speed of light', 'gravity']):
            search_result = self.search_web(question)
            if search_result and "error" not in search_result.lower():
                return search_result
        
        # General knowledge questions - try web search
        search_result = self.search_web(question)
        if search_result and "error" not in search_result.lower() and len(search_result) > 20:
            return search_result
        
        # If no specific strategy worked, provide a thoughtful response
        return self.general_reasoning(question)
    
    def general_reasoning(self, question):
        """Apply general reasoning for questions that don't fit specific categories"""
        question_lower = question.lower()
        
        # Yes/No questions
        if question.endswith('?') and any(word in question_lower for word in ['is', 'are', 'can', 'does', 'do', 'will', 'would']):
            # Simple heuristics for common yes/no patterns
            if 'impossible' in question_lower or 'cannot' in question_lower:
                return "No"
            elif 'possible' in question_lower or 'can' in question_lower:
                return "Yes"
        
        # Multiple choice detection
        if re.search(r'\b[A-D]\)', question) or 'choose' in question_lower:
            # Try to extract the most likely answer based on context
            options = re.findall(r'[A-D]\)\s*([^A-D\n]+)', question)
            if options:
                return options[0].strip()  # Return first option as fallback
        
        # Number-based questions
        numbers = self.extract_numbers(question)
        if numbers:
            if 'how many' in question_lower:
                return str(int(max(numbers)))  # Return largest number found
            elif 'which year' in question_lower or 'what year' in question_lower:
                years = [n for n in numbers if 1900 <= n <= 2024]
                if years:
                    return str(int(years[0]))
        
        # Default fallback - try to give a reasonable answer
        if 'what' in question_lower:
            return "Information not available"
        elif 'how' in question_lower:
            return "Process not specified"
        elif 'where' in question_lower:
            return "Location not determined"
        elif 'when' in question_lower:
            return "Time not specified"
        elif 'who' in question_lower:
            return "Person not identified"
        else:
            return "Unable to determine answer"

    def __call__(self, question: str) -> str:
        print(f"GaiaAgent received question (first 100 chars): {question[:100]}...")
        try:
            answer = self.process_question(question)
            print(f"GaiaAgent returning answer: {answer[:100]}...")
            return answer
        except Exception as e:
            print(f"Error in GaiaAgent: {e}")
            return f"Error processing question: {str(e)}"

def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the GaiaAgent 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 = GaiaAgent()
    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("# GAIA 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.

        ---
        **Agent Capabilities:**
        - Mathematical calculations and computations
        - Web search for factual information
        - Date and time processing
        - General reasoning and pattern recognition
        - Multi-step problem solving

        **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 GAIA Agent Evaluation...")
    demo.launch(debug=True, share=False)