app.py

import os
import requests
import json
import gradio as gr
import pandas as pd
from smolagents import CodeAgent, InferenceClientModel, DuckDuckGoSearchTool
from typing import List, Dict, Any
from dotenv import load_dotenv
import mimetypes # <-- ADDED

# --- 1. SETUP: API, AUTH & AGENT CONFIG ---

# Load .env file for local development (HF Spaces uses Secrets)
load_dotenv()

# !! IMPORTANT !!
# API_BASE_URL: This should be the URL from the course instructions.
API_BASE_URL = os.getenv("API_BASE_URL", "https://agents-course-unit4-scoring.hf.space")

# !! IMPORTANT !!
# YOUR_HF_SPACE_URL: This must be the /tree/main URL of your HF Space.
# e.g., "https://huggingface.co/spaces/YourName/YourSpaceName/tree/main"
# We try to get it from the SPACE_ID, but set a default.
DEFAULT_HF_SPACE_URL = "https://huggingface.co/spaces/tuannhtn80/my-agents-course-final-project/tree/main" # Set a placeholder
#YOUR_HF_SPACE_URL = os.getenv("YOUR_HF_SPACE_URL", DEFAULT_HF_SPACE_URL)
YOUR_HF_SPACE_URL = "https://huggingface.co/spaces/tuannhtn80/my-agents-course-final-project/tree/main"
# Get the Hugging Face token from the Space's Secrets
#HF_TOKEN = os.getenv("HF_TOKEN")
HF_TOKEN = os.environ.get("HF_TOKEN")
if not HF_TOKEN:
    print("WARNING: HF_TOKEN not found in environment variables.")
    print("Please add your Hugging Face token as a Secret in this Space.")
    print("Go to Settings > Secrets > New secret")
    print("Name: HF_TOKEN, Value: hf_...")

# Define API endpoints
QUESTIONS_URL = f"{API_BASE_URL}/questions"
SUBMIT_URL = f"{API_BASE_URL}/submit"

# Constants for file saving
QUESTIONS_FILE = "questions.json"
ANSWERS_FILE = "answers.json"

from smolagents import (
    CodeAgent, 
    DuckDuckGoSearchTool,
    PythonInterpreterTool,
    tool,
    InferenceClientModel
)
from typing import List, Dict, Any, Optional
import os
import tempfile
import re
import json
import requests
from urllib.parse import urlparse

@tool
def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
    """
    Save content to a temporary file and return the path.
    Useful for processing files from the GAIA API.
    
    Args:
        content: The content to save to the file
        filename: Optional filename, will generate a random name if not provided
        
    Returns:
        Path to the saved file
    """
    temp_dir = tempfile.gettempdir()
    if filename is None:
        temp_file = tempfile.NamedTemporaryFile(delete=False)
        filepath = temp_file.name
    else:
        filepath = os.path.join(temp_dir, filename)
    
    # Write content to the file
    with open(filepath, 'w') as f:
        f.write(content)
    
    return f"File saved to {filepath}. You can read this file to process its contents."

@tool
def download_file_from_url(url: str, filename: Optional[str] = None) -> str:
    """
    Download a file from a URL and save it to a temporary location.
    
    Args:
        url: The URL to download from
        filename: Optional filename, will generate one based on URL if not provided
        
    Returns:
        Path to the downloaded file
    """
    try:
        # Parse URL to get filename if not provided
        if not filename:
            path = urlparse(url).path
            filename = os.path.basename(path)
            if not filename:
                # Generate a random name if we couldn't extract one
                import uuid
                filename = f"downloaded_{uuid.uuid4().hex[:8]}"
        
        # Create temporary file
        temp_dir = tempfile.gettempdir()
        filepath = os.path.join(temp_dir, filename)
        
        # Download the file
        response = requests.get(url, stream=True)
        response.raise_for_status()
        
        # Save the file
        with open(filepath, 'wb') as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)
        
        return f"File downloaded to {filepath}. You can now process this file."
    except Exception as e:
        return f"Error downloading file: {str(e)}"

@tool
def extract_text_from_image(image_path: str) -> str:
    """
    Extract text from an image using pytesseract (if available).
    
    Args:
        image_path: Path to the image file
        
    Returns:
        Extracted text or error message
    """
    try:
        # Try to import pytesseract
        import pytesseract
        from PIL import Image
        
        # Open the image
        image = Image.open(image_path)
        
        # Extract text
        text = pytesseract.image_to_string(image)
        
        return f"Extracted text from image:\n\n{text}"
    except ImportError:
        return "Error: pytesseract is not installed. Please install it with 'pip install pytesseract' and ensure Tesseract OCR is installed on your system."
    except Exception as e:
        return f"Error extracting text from image: {str(e)}"

@tool
def analyze_csv_file(file_path: str, query: str) -> str:
    """
    Analyze a CSV file using pandas and answer a question about it.
    
    Args:
        file_path: Path to the CSV file
        query: Question about the data
        
    Returns:
        Analysis result or error message
    """
    try:
        import pandas as pd
        
        # Read the CSV file
        df = pd.read_csv(file_path)
        
        # Run various analyses based on the query
        result = f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
        result += f"Columns: {', '.join(df.columns)}\n\n"
        
        # Add summary statistics
        result += "Summary statistics:\n"
        result += str(df.describe())
        
        return result
    except ImportError:
        return "Error: pandas is not installed. Please install it with 'pip install pandas'."
    except Exception as e:
        return f"Error analyzing CSV file: {str(e)}"

@tool
def analyze_excel_file(file_path: str, query: str) -> str:
    """
    Analyze an Excel file using pandas and answer a question about it.
    
    Args:
        file_path: Path to the Excel file
        query: Question about the data
        
    Returns:
        Analysis result or error message
    """
    try:
        import pandas as pd
        
        # Read the Excel file
        df = pd.read_excel(file_path)
        
        # Run various analyses based on the query
        result = f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
        result += f"Columns: {', '.join(df.columns)}\n\n"
        
        # Add summary statistics
        result += "Summary statistics:\n"
        result += str(df.describe())
        
        return result
    except ImportError:
        return "Error: pandas and openpyxl are not installed. Please install them with 'pip install pandas openpyxl'."
    except Exception as e:
        return f"Error analyzing Excel file: {str(e)}"

class GAIAAgent:
    def __init__(
        self, 
        model_type: str = "InferenceClientModel", 
        model_id: Optional[str] = None,
        api_key: Optional[str] = None,
        api_base: Optional[str] = None,
        temperature: float = 0.2,
        executor_type: str = "local",  # Changed from use_e2b to executor_type
        additional_imports: List[str] = None,
        additional_tools: List[Any] = None,
        system_prompt: Optional[str] = None,  # We'll still accept this parameter but not use it directly
        verbose: bool = False,
        provider: Optional[str] = None,  # Add provider for InferenceClientModel
        timeout: Optional[int] = None   # Add timeout for InferenceClientModel
    ):
        """
        Initialize a GAIAAgent with specified configuration
        
        Args:
            model_type: Type of model to use (InferenceClientModel)
            model_id: ID of the model to use
            api_key: API key for the model provider
            api_base: Base URL for API calls
            temperature: Temperature for text generation
            executor_type: Type of executor for code execution ('local' or 'e2b')
            additional_imports: Additional Python modules to allow importing
            additional_tools: Additional tools to provide to the agent
            system_prompt: Custom system prompt to use (not directly used, kept for backward compatibility)
            verbose: Enable verbose logging
            provider: Provider for InferenceClientModel (e.g., "hf-inference")
            timeout: Timeout in seconds for API calls
        """
        # Set verbosity
        self.verbose = verbose
        self.system_prompt = system_prompt  # Store for potential future use
        
        # Initialize model based on configuration
        if model_type == "InferenceClientModel":
            if api_key is None:
                api_key = os.getenv("HF_TOKEN")
                if not api_key:
                    raise ValueError("No Hugging Face token provided. Please set HUGGINGFACEHUB_API_TOKEN environment variable or pass api_key parameter.")
            
            if self.verbose:
                print(f"Using Hugging Face token: {api_key[:5]}...")
                
            self.model = InferenceClientModel(
                #model_id=model_id or "meta-llama/Llama-3-70B-Instruct",
                #model_id = "Qwen/Qwen3-Next-80B-A3B-Instruct",
                #model_id = "Qwen/Qwen3-VL-30B-A3B-Instruct",
                #model_id = "meta-llama/Llama-3-70B-Instruct",
                model_id = "deepseek-ai/DeepSeek-V3.1",
                #provider=provider or "hf-inference",
                token=api_key,
                timeout=timeout or 120,
                temperature=temperature
            )
        else:
            raise ValueError(f"Unknown model type: {model_type}")
        
        if self.verbose:
            print(f"Initialized model: {model_type} - {model_id}")
        
        # Initialize default tools
        self.tools = [
            DuckDuckGoSearchTool(),
            PythonInterpreterTool(),
            save_and_read_file,
            download_file_from_url,
            analyze_csv_file,
            analyze_excel_file
        ]
        
        # Add extract_text_from_image if PIL and pytesseract are available
        try:
            import pytesseract
            from PIL import Image
            self.tools.append(extract_text_from_image)
            if self.verbose:
                print("Added image processing tool")
        except ImportError:
            if self.verbose:
                print("Image processing libraries not available")
        
        # Add any additional tools
        if additional_tools:
            self.tools.extend(additional_tools)
            
        if self.verbose:
            print(f"Initialized with {len(self.tools)} tools")
        
        # Setup imports allowed
        self.imports = ["pandas", "numpy", "datetime", "json", "re", "math", "os", "requests", "csv", "urllib"]
        if additional_imports:
            self.imports.extend(additional_imports)
            
        # Initialize the CodeAgent
        executor_kwargs = {}
        if executor_type == "e2b":
            try:
                # Try to import e2b dependencies to check if they're available
                from e2b_code_interpreter import Sandbox
                if self.verbose:
                    print("Using e2b executor")
            except ImportError:
                if self.verbose:
                    print("e2b dependencies not found, falling back to local executor")
                executor_type = "local"  # Fallback to local if e2b is not available
        
        self.agent = CodeAgent(
            tools=self.tools,
            model=self.model,
            additional_authorized_imports=self.imports,
            executor_type=executor_type,
            executor_kwargs=executor_kwargs,
            verbosity_level=2 if self.verbose else 0
        )
        
        if self.verbose:
            print("Agent initialized and ready")
    
    def answer_question(self, question: str, task_file_path: Optional[str] = None) -> str:
        """
        Process a GAIA benchmark question and return the answer
        
        Args:
            question: The question to answer
            task_file_path: Optional path to a file associated with the question
            
        Returns:
            The answer to the question
        """
        try:
            if self.verbose:
                print(f"Processing question: {question}")
                if task_file_path:
                    print(f"With associated file: {task_file_path}")
            
            # Create a context with file information if available
            context = question
            file_content = None
            
            # If there's a file, read it and include its content in the context
            if task_file_path:
                try:
                    with open(task_file_path, 'r') as f:
                        file_content = f.read()
                    
                    # Determine file type from extension
                    import os
                    file_ext = os.path.splitext(task_file_path)[1].lower()
                    
                    context = f"""
Question: {question}
This question has an associated file. Here is the file content:
```{file_ext}
{file_content}
```
Analyze the file content above to answer the question.
"""
                except Exception as file_e:
                    context = f"""
Question: {question}
This question has an associated file at path: {task_file_path}
However, there was an error reading the file: {file_e}
You can still try to answer the question based on the information provided.
"""
            
            # Check for special cases that need specific formatting
            # Reversed text questions
            if question.startswith(".") or ".rewsna eht sa" in question:
                context = f"""
This question appears to be in reversed text. Here's the reversed version:
{question[::-1]}
Now answer the question above. Remember to format your answer exactly as requested.
"""
            
            # Add a prompt to ensure precise answers
            full_prompt = f"""{context}
When answering, provide ONLY the precise answer requested. 
Do not include explanations, steps, reasoning, or additional text.
Be direct and specific. GAIA benchmark requires exact matching answers.
For example, if asked "What is the capital of France?", respond simply with "Paris".
"""
            
            # Run the agent with the question
            answer = self.agent.run(full_prompt)
            
            # Clean up the answer to ensure it's in the expected format
            # Remove common prefixes that models often add
            answer = self._clean_answer(answer)
            
            if self.verbose:
                print(f"Generated answer: {answer}")
                
            return answer
        except Exception as e:
            error_msg = f"Error answering question: {e}"
            if self.verbose:
                print(error_msg)
            return error_msg
    
    def _clean_answer(self, answer: any) -> str:
        """
        Clean up the answer to remove common prefixes and formatting
        that models often add but that can cause exact match failures.
        
        Args:
            answer: The raw answer from the model
            
        Returns:
            The cleaned answer as a string
        """
        # Convert non-string types to strings
        if not isinstance(answer, str):
            # Handle numeric types (float, int)
            if isinstance(answer, float):
                # Format floating point numbers properly
                # Check if it's an integer value in float form (e.g., 12.0)
                if answer.is_integer():
                    formatted_answer = str(int(answer))
                else:
                    # For currency values that might need formatting
                    if abs(answer) >= 1000:
                        formatted_answer = f"${answer:,.2f}"
                    else:
                        formatted_answer = str(answer)
                return formatted_answer
            elif isinstance(answer, int):
                return str(answer)
            else:
                # For any other type
                return str(answer)
        
        # Now we know answer is a string, so we can safely use string methods
        # Normalize whitespace
        answer = answer.strip()
        
        # Remove common prefixes and formatting that models add
        prefixes_to_remove = [
            "The answer is ", 
            "Answer: ",
            "Final answer: ",
            "The result is ",
            "To answer this question: ",
            "Based on the information provided, ",
            "According to the information: ",
        ]
        
        for prefix in prefixes_to_remove:
            if answer.startswith(prefix):
                answer = answer[len(prefix):].strip()
        
        # Remove quotes if they wrap the entire answer
        if (answer.startswith('"') and answer.endswith('"')) or (answer.startswith("'") and answer.endswith("'")):
            answer = answer[1:-1].strip()
        
        return answer
    
# --- 2. AGENT & TOOLS SETUP (from app.py) ---

def setup_agent() -> GAIAAgent:
    """
    Initializes and configures the smol-agent.
    """
    print("Setting up agent...")

    if not HF_TOKEN:
        raise ValueError("HF_TOKEN is not set. Cannot initialize agent model.")

    # Instantiate the GAIA-specific agent
    agent = GAIAAgent(
        model_id = "Qwen/Qwen3-Next-80B-A3B-Instruct",
        api_key=HF_TOKEN,
        verbose=True
    )

    print("Agent setup complete.")
    return agent

# ==============================================================================
# ===== START OF UPDATED SECTION =================================================
# ==============================================================================

def generate_system_prompt(task_id: str, question: str) -> str:
    """
    Creates a specific system prompt for the agent,
    optimized for direct answers and OCR tasks.
    """
    return f"""
    You are an expert AI agent. Your sole purpose is to solve a task and provide a single, exact, and final answer.
    
    **CRITICAL RULES:**
    1.  You MUST write and execute Python code to solve the problem.
    2.  You MUST follow the "HOW TO WORK" steps.
    3.  Your final response MUST be ONLY the answer. No "Thought", "Action", "Final Answer:", or any other text.
    
    **TASK DETAILS:**
    -   **Question:** "{question}"
    -   **Task ID:** {task_id}

    **HOW TO WORK:**
    1.  **Think (Internal)**: Analyze the question. (Do not output this thought).
    2.  **Search (If Needed)**: Use `duckduckgo_search` if you need external information.
    3.  **Download File**: You MUST download the relevant file using this exact Python code block. The file's name will be stored in the `file_name` variable.
        ```python
        import requests
        import mimetypes
        
        file_url = f"{API_BASE_URL}/files/{task_id}"
        print(f"Downloading file from {{file_url}}...") # Use {{}} to escape f-string
        response = requests.get(file_url)
        
        # Determine file extension
        content_type = response.headers.get('content-type')
        extension = mimetypes.guess_extension(content_type)
        if not extension:
            # Fallback logic from original prompt
            if 'csv' in content_type:
                extension = '.csv'
            elif 'excel' in content_type or 'spreadsheetml' in content_type:
                extension = '.xlsx'
            elif 'jpeg' in content_type:
                extension = '.jpg'
            elif 'png' in content_type:
                extension = '.png'
            else:
                extension = '.bin' # default
        
        file_name = "downloaded_file" + extension
        with open(file_name, 'wb') as f:
            f.write(response.content)
        print(f"File saved as {{file_name}}") # Use {{}} to escape f-string
        print(f"File content-type: {{content_type}}")
        ```
    4.  **Analyze File with Code**: After downloading, use the `file_name` variable to analyze the file.
        -   **For CSV/Excel files (e.g., .csv, .xlsx)**:
            ```python
            !pip install pandas openpyxl
            import pandas as pd
            
            # The 'file_name' variable comes from the download step
            try:
                if file_name.endswith('.csv'):
                    df = pd.read_csv(file_name)
                else:
                    df = pd.read_excel(file_name)
                print(df.head())
                # ... write more code to get the answer from the 'df' dataframe
            except Exception as e:
                print(f"Error reading file with pandas: {{e}}")
            ```
        -   **For Image files (e.g., .jpg, .png)**:
            ```python
            # Step 1: Install Tesseract-OCR system package
            !apt-get update && apt-get install -y tesseract-ocr
            
            # Step 2: Install Python libraries
            !pip install Pillow pytesseract
            
            import pytesseract
            from PIL import Image
            
            # The 'file_name' variable comes from the download step
            try:
                img = Image.open(file_name)
                text_from_image = pytesseract.image_to_string(img)
                print("--- OCR Result ---")
                print(text_from_image)
                print("------------------")
                # ... write more code to analyze 'text_from_image' to find the answer
            except Exception as e:
                print(f"Error processing image with OCR: {{e}}")
            ```
    5.  **Produce Final Answer**: After all steps, your code must find the answer. Your *final* output message MUST be *only* this answer.
        -   **Example of your internal code (DO NOT OUTPUT):**
            ```python
            # ... code to find the answer ...
            final_answer = 42
            print(final_answer) # This is how you print the final answer
            ```
        -   **Your Final Response (Correct):**
            `42`
        -   **Your Final Response (INCORRECT):**
            `Thought: The final answer is 42. Final Answer: 42`
    """

# ==============================================================================
# ===== END OF UPDATED SECTION ===================================================
# ==============================================================================


# --- 3. CORE CHALLENGE LOGIC (from app.py, adapted for Gradio) ---

def get_questions() -> List[Dict[str, Any]]:
    """
    Fetches the list of questions from the challenge API.
    Also saves the questions to a local `questions.json` file.
    """
    print(f"Fetching questions from {QUESTIONS_URL}...")
    try:
        response = requests.get(QUESTIONS_URL)
        response.raise_for_status() # Raise an error for bad responses
        questions = response.json()
        print(f"Successfully fetched {len(questions)} questions.")

        # Save questions to a file
        try:
            with open(QUESTIONS_FILE, 'w') as f:
                json.dump(questions, f, indent=4)
            print(f"Questions saved to {QUESTIONS_FILE}")
        except Exception as e:
            print(f"Warning: Could not save questions to {QUESTIONS_FILE}. Error: {e}")

        return questions
    except Exception as e:
        print(f"Error fetching questions: {e}")
        raise  # Re-raise exception to be caught by Gradio handler

def process_question(agent: GAIAAgent, task: Dict[str, Any], all_answers: List[Dict[str, str]]) -> Dict[str, str]:
    """
    Uses the agent to process a single question and get an answer.
    Saves incremental progress to answers.json.
    (This is from app.py)
    """
    task_id = task.get("task_id")
    question = task.get("question")
    
    print(f"\n--- Processing Task {task_id} ---")
    print(f"Question: {question}")
    
    try:
        # Use the GAIAAgent's dedicated method to answer the question
        answer = agent.answer_question(question=question)
        
        # Clean up the answer (just in case)
        answer = str(answer).strip()
        print(f"Agent's Answer: {answer}")
        
        answer_dict = {
            "task_id": task_id,
            "submitted_answer": answer
        }
        
    except Exception as e:
        print(f"Error processing task {task_id}: {e}")
        answer_dict = {
            "task_id": task_id,
            "submitted_answer": "ERROR_PROCESSING"
        }
    
    # Save progress incrementally
    all_answers.append(answer_dict)
    try:
        with open(ANSWERS_FILE, 'w') as f:
            json.dump(all_answers, f, indent=4)
    except Exception as e:
        print(f"Warning: Could not save incremental answers to {ANSWERS_FILE}. Error: {e}")
    
    return answer_dict

def submit_results(username: str, agent_code_url: str, answers: List[Dict[str, str]]) -> str:
    """
    Submits the collected answers to the API and returns a status string.
    (This combines logic from both files)
    """
    print(f"\n--- Submitting {len(answers)} Answers ---")
    
    # Format the payload as required by the challenge
    submission_payload = {
        "username": username,
        "agent_code": agent_code_url,
        "answers": answers
    }
    
    try:
        response = requests.post(SUBMIT_URL, json=submission_payload, timeout=60)
        response.raise_for_status()
        
        result = response.json()
        
        print("Submission Successful!")
        final_status = (
            f"Submission Successful!\n"
            f"User: {result.get('username')}\n"
            f"Overall Score: {result.get('score', 'N/A')}% "
            f"({result.get('correct_count', '?')}/{result.get('total_attempted', '?')} correct)\n"
            f"Message: {result.get('message', 'No message received.')}"
        )
        return final_status
        
    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)
        return status_message
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        return status_message
    except Exception as e:
        status_message = f"Error submitting results: {e}"
        print(status_message)
        return status_message

def get_agent_code_url() -> str:
    """Helper to determine the agent code URL."""
    space_id = os.getenv("SPACE_ID")
    if YOUR_HF_SPACE_URL != DEFAULT_HF_SPACE_URL:
        return YOUR_HF_SPACE_URL
    elif space_id:
        return f"https://huggingface.co/spaces/{space_id}/tree/main"
    else:
        print("Warning: Could not determine HF Space URL. Using default.")
        return DEFAULT_HF_SPACE_URL

# --- 4. GRADIO INTERFACE FUNCTIONS ---

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the REAL agent on them, saves answers,
    submits all answers, and displays the results.
    (This is the modified function from app0-submit.py)
    """
    if not profile:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None
    
    username = f"{profile.username}"
    print(f"User logged in: {username}")
    
    agent_code_url = get_agent_code_url()
    #if agent_code_url == DEFAULT_HF_SPACE_URL:
    #    print(agent_code_url)
    #    return "ERROR: `YOUR_HF_SPACE_URL` is not set correctly. Please update the variable in the code.", None

    # 1. Fetch Questions
    try:
        questions_data = get_questions()
        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 Exception as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None

    # 2. Instantiate REAL Agent
    try:
        agent = setup_agent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e} (Did you set HF_TOKEN secret?)", None

    # 3. Run your Agent
    results_log = []
    all_answers = [] # This list will be populated by process_question
    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
        
        # process_question runs the agent AND saves incrementally to all_answers
        answer_dict = process_question(agent, item, all_answers)
        
        results_log.append({
            "Task ID": task_id,
            "Question": question_text,
            "Submitted Answer": answer_dict.get("submitted_answer")
        })

    if not all_answers:
        print("Agent did not produce any answers to submit.")
        return "Agent did to produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Submit
    status_update = f"Agent finished. Submitting {len(all_answers)} answers for user '{username}'..."
    print(status_update)
    
    final_status = submit_results(username, agent_code_url, all_answers)
    results_df = pd.DataFrame(results_log)
    return final_status, results_df

def submit_from_file_gradio(profile: gr.OAuthProfile | None):
    """
    Loads answers from ANSWERS_FILE and submits them.
    (This is the new function for the "submit only" button)
    """
    if not profile:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    username = f"{profile.username}"
    print(f"User logged in: {username}")
    
    agent_code_url = get_agent_code_url()
    #if agent_code_url == DEFAULT_HF_SPACE_URL:
    #    print(agent_code_url)
    #    return "ERROR: `YOUR_HF_SPACE_URL` is not set correctly. Please update the variable in the code.", None

    print(f"\n--- Submitting Answers from {ANSWERS_FILE} ---")
    try:
        with open(ANSWERS_FILE, 'r') as f:
            answers = json.load(f)
        
        if not answers:
            print(f"No answers found in {ANSWERS_FILE}. Exiting.")
            return f"Error: No answers found in {ANSWERS_FILE}.", None
            
        print(f"Loaded {len(answers)} answers from {ANSWERS_FILE}.")
        
        # Call the submission function
        final_status = submit_results(username, agent_code_url, answers)
        
        # Create a dataframe from the loaded answers for display
        results_log = []
        try:
            # Try to load questions to make a nice table
            with open(QUESTIONS_FILE, 'r') as qf:
                questions = {q['task_id']: q['question'] for q in json.load(qf)}
            
            for ans in answers:
                results_log.append({
                    "Task ID": ans['task_id'],
                    "Question": questions.get(ans['task_id'], "Question not found in cache"),
                    "Submitted Answer": ans['submitted_answer']
                })
            results_df = pd.DataFrame(results_log)
        except Exception as e:
            print(f"Could not load questions file for display: {e}")
            results_df = pd.DataFrame(answers) # Fallback to just answers

        return final_status, results_df

    except FileNotFoundError:
        error_msg = f"Error: The file '{ANSWERS_FILE}' was not found. Cannot submit."
        print(error_msg)
        return error_msg, None
    except Exception as e:
        error_msg = f"An error occurred while reading or submitting answers from file: {e}"
        print(error_msg)
        return error_msg, None

# --- 5. BUILD GRADIO INTERFACE (from app0-submit.py) ---

with gr.Blocks() as demo:
    gr.Markdown("# Advanced Agent 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 all questions.
            * Run the full agent on every question (This will take a long time!).
            * Save answers to `answers.json`.
            * Submit all answers and get your score.
        3.  Click **'Submit from answers.json (no re-run)'** to:
            * Load answers from the `answers.json` file (if it exists).
            * Submit those answers without re-running the agent. This is much faster.
        """
    )

    gr.LoginButton()

    with gr.Row():
        run_button = gr.Button("Run Evaluation & Submit All Answers")
        submit_file_button = gr.Button("Submit from answers.json (no re-run)")

    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]
    )
    
    submit_file_button.click(
        fn=submit_from_file_gradio,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    
    # Check for SPACE_ID at startup for information
    space_id_startup = os.getenv("SPACE_ID") 
    if space_id_startup:
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        # Set the default HF Space URL if it's not already set
        if YOUR_HF_SPACE_URL == DEFAULT_HF_SPACE_URL:
            YOUR_HF_SPACE_URL = f"https://huggingface.co/spaces/{space_id_startup}/tree/main"
            print(f"   Set YOUR_HF_SPACE_URL to: {YOUR_HF_SPACE_URL}")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?).")
        if YOUR_HF_SPACE_URL == DEFAULT_HF_SPACE_URL:
             print("ERROR: `YOUR_HF_SPACE_URL` is not set.")
             print("Please set it in your .env file or as a script variable.")

    print(f"API_BASE_URL: {API_BASE_URL}")
    print(f"Using Agent Code URL: {YOUR_HF_SPACE_URL}")
    print("-"*(60 + len(" App Starting ")) + "\n")

    print("Launching Gradio Interface...")
    demo.launch(debug=True, share=False)