app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
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"

# Replace BasicAgent with your SmolaAgent in the run_and_submit_all function
def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the SmolaAgent 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
    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. 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")
        
        # Check if there are files associated with this task
        try:
            files_url = f"{api_url}/files/{task_id}"
            files_response = requests.get(files_url, timeout=15)
            if files_response.status_code == 200:
                # Save the file and provide its path to the agent
                # This depends on what format the files are returned in
                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}")
            # Continue even if file check fails
        
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        
        try:
            # Get full agent response
            full_response = agent(question_text)
            
            # Extract just the final answer part for submission
            submitted_answer = extract_final_answer(full_response)
                
            # Add to submission payload
            answers_payload.append({
                "task_id": task_id, 
                "submitted_answer": submitted_answer,
                "reasoning_trace": full_response  # Optional: include full reasoning
            })
            
            # 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}")
            
        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("# 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)