app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import json
import time
import sys
from pathlib import Path

# Fix cookies import by creating a module structure dynamically
current_dir = os.path.dirname(os.path.abspath(__file__))
if current_dir not in sys.path:
    sys.path.insert(0, current_dir)

# Create __init__.py file if it doesn't exist
init_path = os.path.join(current_dir, "__init__.py")
if not os.path.exists(init_path):
    with open(init_path, "w") as f:
        f.write("")  # Create empty __init__.py file

# Now imports should work
try:
    from cookies import COOKIES
    # Test the import to ensure it works
    print("Successfully imported COOKIES")
except ImportError as e:
    print(f"Error importing COOKIES: {e}")
    # If import fails, try a direct import with modified sys.modules
    import cookies
    sys.modules[__name__ + '.cookies'] = cookies
    print("Added cookies to sys.modules")

# Now the rest of your imports should work
from dotenv import load_dotenv
from huggingface_hub import login
from text_inspector_tool import TextInspectorTool
from text_web_browser import (
    ArchiveSearchTool,
    FinderTool,
    FindNextTool,
    PageDownTool,
    PageUpTool,
    SimpleTextBrowser,
    VisitTool,
)
from visual_qa import visualizer
from reformulator import prepare_response

from smolagents import (
    CodeAgent,
    GoogleSearchTool,
    LiteLLMModel,
    ToolCallingAgent,
)

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

# GAIA system prompt for exact answer format
GAIA_SYSTEM_PROMPT = """You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."""

# --- Smolagent Implementation ---
load_dotenv(override=True)

# Try to login with HF token from env or secrets
try:
    hf_token = os.getenv("HF_TOKEN")
    if hf_token:
        login(hf_token)
        print("Successfully logged in to Hugging Face")
    else:
        print("No HF_TOKEN found in environment")
except Exception as e:
    print(f"Error logging in to Hugging Face: {e}")

# Custom settings for your agent
custom_role_conversions = {"tool-call": "assistant", "tool-response": "user"}
user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0"

BROWSER_CONFIG = {
    "viewport_size": 1024 * 5,
    "downloads_folder": "downloads_folder",
    "request_kwargs": {
        "headers": {"User-Agent": user_agent},
        "timeout": 300,
    },
    "serpapi_key": os.getenv("SERPAPI_API_KEY"),
}

# Create downloads folder if it doesn't exist
os.makedirs(f"./{BROWSER_CONFIG['downloads_folder']}", exist_ok=True)

class SmolaAgent:
    def __init__(self):
        print("Initializing SmolaAgent...")
        
        # Initialize model
        model_id = "o1"  # You can adjust this or make it configurable
        model_params = {
            "model_id": model_id,
            "custom_role_conversions": custom_role_conversions,
            "max_completion_tokens": 8192,
        }
        if model_id == "o1":
            model_params["reasoning_effort"] = "high"
        
        self.model = LiteLLMModel(**model_params)
        
        # Create agent with tools
        text_limit = 100000
        browser = SimpleTextBrowser(**BROWSER_CONFIG)
        WEB_TOOLS = [
            GoogleSearchTool(provider="serper"),
            VisitTool(browser),
            PageUpTool(browser),
            PageDownTool(browser),
            FinderTool(browser),
            FindNextTool(browser),
            ArchiveSearchTool(browser),
            TextInspectorTool(self.model, text_limit),
        ]
        
        # Create text webbrowser agent
        self.text_webbrowser_agent = ToolCallingAgent(
            model=self.model,
            tools=WEB_TOOLS,
            max_steps=20,
            verbosity_level=2,
            planning_interval=4,
            name="search_agent",
            description="""A team member that will search the internet to answer your question.
        Ask him for all your questions that require browsing the web.
        Provide him as much context as possible, in particular if you need to search on a specific timeframe!
        And don't hesitate to provide him with a complex search task, like finding a difference between two webpages.
        Your request must be a real sentence, not a google search! Like "Find me this information (...)" rather than a few keywords.
        """,
            provide_run_summary=True,
        )
        
        self.text_webbrowser_agent.prompt_templates["managed_agent"]["task"] += """You can navigate to .txt online files.
        If a non-html page is in another format, especially .pdf or a Youtube video, use tool 'inspect_file_as_text' to inspect it.
        Additionally, if after some searching you find out that you need more information to answer the question, you can use `final_answer` with your request for clarification as argument to request for more information."""

        # Create manager agent
        self.manager_agent = CodeAgent(
            model=self.model,
            tools=[visualizer, TextInspectorTool(self.model, text_limit)],
            max_steps=12,
            verbosity_level=2,
            additional_authorized_imports=["*"],
            planning_interval=4,
            managed_agents=[self.text_webbrowser_agent],
        )
        
        print("SmolaAgent initialized successfully.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question: {question[:50]}...")
        
        # Include the GAIA system prompt in the question to ensure proper answer format
        augmented_question = f"""You have one question to answer. It is paramount that you provide a correct answer.
Give it all you can: I know for a fact that you have access to all the relevant tools to solve it and find the correct answer (the answer does exist). Failure or 'I cannot answer' or 'None found' will not be tolerated, success will be rewarded.
Run verification steps if that's needed, you must make sure you find the correct answer!

{GAIA_SYSTEM_PROMPT}

Here is the task:
{question}"""
        
        try:
            # Run the agent
            result = self.manager_agent.run(augmented_question)
            
            # Use reformulator to get properly formatted final answer
            agent_memory = self.manager_agent.write_memory_to_messages()
            
            # Add the GAIA system prompt to the reformulation to ensure correct format
            for message in agent_memory:
                if message.get("role") == "system" and message.get("content"):
                    if isinstance(message["content"], list):
                        for content_item in message["content"]:
                            if content_item.get("type") == "text":
                                content_item["text"] = GAIA_SYSTEM_PROMPT + "\n\n" + content_item["text"]
                    else:
                        message["content"] = GAIA_SYSTEM_PROMPT + "\n\n" + message["content"]
                    break
            
            final_answer = prepare_response(augmented_question, agent_memory, self.model)
            
            print(f"Agent returning answer: {final_answer}")
            return final_answer
        
        except Exception as e:
            print(f"Error running agent: {e}")
            return "FINAL ANSWER: Unable to determine"

# Function to extract the exact answer from agent response
def extract_final_answer(agent_response):
    if "FINAL ANSWER:" in agent_response:
        answer = agent_response.split("FINAL ANSWER:")[1].strip()
        
        # Additional cleaning to ensure exact match
        # Remove any trailing punctuation
        answer = answer.rstrip('.,!?;:')
        
        # Clean numbers (remove commas and units)
        # This is a simple example - you might need more sophisticated cleaning
        words = answer.split()
        for i, word in enumerate(words):
            # Try to convert to a number to remove commas and format correctly
            try:
                num = float(word.replace(',', '').replace('$', '').replace('%', ''))
                # Convert to int if it's a whole number
                words[i] = str(int(num)) if num.is_integer() else str(num)
            except (ValueError, AttributeError):
                # Not a number, leave as is
                pass
        
        return ' '.join(words)
    
    return "Unable to determine"

# Simple rate-limited request function with retry
def make_rate_limited_request(url, method="GET", max_retries=5, initial_wait=5, **kwargs):
    """
    Makes HTTP requests with automatic handling of rate limits (429)
    
    Args:
        url: The URL to request
        method: HTTP method (GET, POST, etc.)
        max_retries: Maximum number of retries for rate limit errors
        initial_wait: Initial wait time in seconds, doubled on each retry
        **kwargs: Additional arguments to pass to requests.request
        
    Returns:
        requests.Response object on success
        
    Raises:
        Exception if max_retries is exceeded
    """
    wait_time = initial_wait
    
    for attempt in range(max_retries):
        try:
            response = requests.request(method, url, **kwargs)
            
            # If not rate limited, return the response
            if response.status_code != 429:
                return response
                
            # Handle rate limiting
            retry_after = response.headers.get('Retry-After')
            if retry_after:
                # If server specified wait time, use that
                wait_seconds = int(retry_after)
                print(f"Rate limited. Server requested wait of {wait_seconds} seconds.")
            else:
                # Otherwise use exponential backoff
                wait_seconds = wait_time
                wait_time *= 2  # Double the wait time for next attempt
                print(f"Rate limited. Using exponential backoff: waiting {wait_seconds} seconds.")
            
            # Sleep and retry
            time.sleep(wait_seconds)
            
        except requests.exceptions.RequestException as e:
            print(f"Request error: {e}")
            # For connection errors, wait and retry
            time.sleep(wait_time)
            wait_time *= 2
    
    # If we get here, we've exceeded max_retries
    raise Exception(f"Failed to get a valid response after {max_retries} attempts")

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the SmolaAgent on them, submits all answers,
    and displays the results. Uses caching and handles rate limits.
    """
    # --- 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
    try:
        agent = SmolaAgent()
    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
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    # 2. Use cached questions or fetch with rate limiting
    cache_file = "cached_questions.json"
    
    # Try to load from cache first
    if os.path.exists(cache_file) and os.path.getsize(cache_file) > 10:
        print(f"Loading cached questions from {cache_file}")
        try:
            with open(cache_file, 'r') as f:
                questions_data = json.load(f)
            print(f"Loaded {len(questions_data)} questions from cache")
        except Exception as e:
            print(f"Error loading cached questions: {e}")
            questions_data = None
    else:
        questions_data = None
    
    # Fetch if not cached
    if not questions_data:
        print("Fetching questions with rate limit handling...")
        try:
            # Manually implement a retry with long waits
            max_attempts = 5
            base_wait = 20  # Start with a long wait time
            
            for attempt in range(max_attempts):
                print(f"Attempt {attempt+1}/{max_attempts} to fetch questions")
                
                try:
                    response = requests.get(questions_url, timeout=15)
                    
                    if response.status_code == 200:
                        questions_data = response.json()
                        print(f"Successfully fetched {len(questions_data)} questions")
                        
                        # Cache for future use
                        try:
                            with open(cache_file, 'w') as f:
                                json.dump(questions_data, f)
                            print(f"Cached {len(questions_data)} questions to {cache_file}")
                        except Exception as e:
                            print(f"Warning: Failed to cache questions: {e}")
                            
                        break  # Success, exit retry loop
                        
                    elif response.status_code == 429:
                        wait_time = base_wait * (2 ** attempt)
                        print(f"Rate limited (429). Waiting {wait_time} seconds before retry...")
                        time.sleep(wait_time)
                    else:
                        print(f"Unexpected status code: {response.status_code}")
                        time.sleep(base_wait)
                        
                except requests.exceptions.RequestException as e:
                    print(f"Request error: {e}")
                    time.sleep(base_wait)
            
            if not questions_data:
                return "Failed to fetch questions after multiple attempts. Please try again later.", None
                
        except Exception as e:
            print(f"Error fetching questions: {e}")
            return f"Error fetching questions: {e}", None

    # 3. Run your Agent
    results_log = []
    answers_payload = []
    answers_cache_file = "cached_answers.json"
    
    # Try to load cached answers
    cached_answers = {}
    if os.path.exists(answers_cache_file):
        try:
            with open(answers_cache_file, 'r') as f:
                cached_answers = json.load(f)
            print(f"Loaded {len(cached_answers)} cached answers")
        except Exception as e:
            print(f"Error loading cached answers: {e}")
    
    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
        
        # Check if we already have a cached answer for this task
        if task_id in cached_answers:
            print(f"Using cached answer for task {task_id}")
            full_response = cached_answers[task_id]['full_response']
            submitted_answer = cached_answers[task_id]['submitted_answer']
        else:
            try:
                # Check for associated files with manual retry
                try:
                    files_url = f"{api_url}/files/{task_id}"
                    files_response = requests.get(files_url, timeout=15)
                    if files_response.status_code == 200:
                        print(f"Task {task_id} has associated files")
                        # Handle files if needed
                except Exception as e:
                    print(f"Error checking for files for task {task_id}: {e}")
                
                # Get agent response
                full_response = agent(question_text)
                
                # Extract final answer
                submitted_answer = extract_final_answer(full_response)
                
                # Cache this answer
                cached_answers[task_id] = {
                    'full_response': full_response,
                    'submitted_answer': submitted_answer
                }
                
                # Save to cache after each question to avoid losing progress
                try:
                    with open(answers_cache_file, 'w') as f:
                        json.dump(cached_answers, f)
                except Exception as e:
                    print(f"Warning: Failed to save answer cache: {e}")
                
            except Exception as e:
                print(f"Error running agent on task {task_id}: {e}")
                full_response = f"AGENT ERROR: {e}"
                submitted_answer = "Unable to determine"
        
        # Add to submission payload
        answers_payload.append({
            "task_id": task_id, 
            "submitted_answer": submitted_answer,
            "reasoning_trace": full_response
        })
        
        # Log for display
        results_log.append({
            "Task ID": task_id, 
            "Question": question_text, 
            "Submitted Answer": submitted_answer,
            "Full Response": full_response
        })
        
        print(f"Processed task {task_id}, answer: {submitted_answer}")

    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 with robust retry mechanism
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        # Use manual retry for submission
        max_attempts = 5
        base_wait = 30  # Start with a long wait time
        
        for attempt in range(max_attempts):
            print(f"Submission attempt {attempt+1}/{max_attempts}")
            
            try:
                response = requests.post(submit_url, json=submission_data, timeout=60)
                
                if response.status_code == 200:
                    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
                    
                elif response.status_code == 429:
                    wait_time = base_wait * (2 ** attempt)
                    print(f"Rate limited (429). Waiting {wait_time} seconds before retry...")
                    time.sleep(wait_time)
                else:
                    print(f"Submission failed with status code: {response.status_code}")
                    error_detail = f"Server responded with status {response.status_code}."
                    try:
                        error_json = response.json()
                        error_detail += f" Detail: {error_json.get('detail', response.text)}"
                    except:
                        error_detail += f" Response: {response.text[:500]}"
                    
                    # For non-429 errors, don't retry
                    status_message = f"Submission Failed: {error_detail}"
                    print(status_message)
                    results_df = pd.DataFrame(results_log)
                    return status_message, results_df
                    
            except requests.exceptions.RequestException as e:
                print(f"Request error during submission: {e}")
                time.sleep(base_wait)
        
        # If we get here, all attempts failed
        status_message = f"Submission Failed: Maximum retry attempts exceeded."
        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("# Smolagent GAIA Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Log in to your Hugging Face account using the button below.
        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
        ---
        **Note:** This process will take some time as the agent processes each question. The agent is specifically configured to 
        format answers according to the GAIA benchmark requirements:
        - Numbers: No commas, no units
        - Strings: No articles, no abbreviations
        - Lists: Comma-separated values following the above rules
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    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 Smolagent GAIA Evaluation...")
    demo.launch(debug=True, share=False)