app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import asyncio
from langchain_google_genai import ChatGoogleGenerativeAI
from typing import IO, Dict, TypedDict, Annotated, Sequence, Any, Callable
from io import BytesIO
from langchain_core.messages import HumanMessage, SystemMessage, BaseMessage, AIMessage
from langgraph.graph import StateGraph, END
import base64
from google.ai.generativelanguage_v1beta.types import Tool as GenAITool
import google.generativeai as genai
import operator
from langchain_core.tools import tool
from utilities import get_file
import time
from tenacity import retry, stop_after_attempt, wait_exponential
import json
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Constants
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
GEMINI_API_KEY = os.getenv("Gemini_API_key")
SERPER_API_KEY = os.getenv("SERPER_API_KEY")

# Define the state type
class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], operator.add]
    next: str

# Tool execution function
def execute_tool(tool_name: str, tool_args: Dict[str, Any], tools: Dict[str, Callable]) -> Any:
    """Execute a tool with the given arguments."""
    if tool_name not in tools:
        raise ValueError(f"Tool {tool_name} not found")
    tool_func = tools[tool_name]
    # If the tool is decorated with @tool, use the run method
    if hasattr(tool_func, 'run'):
        return tool_func.run(**tool_args)
    # Otherwise, call the function directly
    return tool_func(**tool_args)

# Convert existing functions to tools
@tool
def analyse_excel(task_id: str) -> Dict[str, float]:
    '''Analyzes the Excel file associated with the given task_id.'''
    excel_file = get_file(task_id)
    df = pd.read_excel(excel_file, sheet_name=0)
    return df.select_dtypes(include='number').sum().to_dict()

@tool
def add_numbers(a: float, b: float) -> float:
    '''Adds two numbers together.'''
    return a + b

@tool
def transcribe_audio(task_id: str) -> HumanMessage:
    '''Transcribes an audio file.'''
    audio_file = get_file(task_id)
    if audio_file is None:
        raise ValueError("No audio file found for the given task_id.")
    audio_file.seek(0)
    encoded_audio = base64.b64encode(audio_file.read()).decode("utf-8")
    return HumanMessage(
        content=[
            {"type": "text", "text": "Transcribe the audio."},
            {
                "type": "media",
                "data": encoded_audio,
                "mime_type": "audio/mpeg",
            },
        ]
    )

@tool
def python_code(task_id: str) -> str:
    '''Returns the Python code associated with the given task_id.'''
    code_request = requests.get(url=f'{DEFAULT_API_URL}/files/{task_id}')
    code_request.raise_for_status()
    return code_request.text

@tool
def open_image(task_id: str) -> str:
    '''Opens an image file associated with the given task_id.'''
    image_file = get_file(task_id)
    if image_file is None:
        raise ValueError("No image file found for the given task_id.")
    return base64.b64encode(image_file.read()).decode("utf-8")

@tool
def open_youtube_video(url: str, query: str) -> str:
    '''Answers a question about a video from the given URL.'''
    client = genai.Client(api_key=GOOGLE_API_KEY)
    response = client.models.generate_content(
        model='models/gemini-2.0-flash',
        contents=types.Content(
            parts=[
                types.Part(file_data=types.FileData(file_uri=url)),
                types.Part(text=f'''{query} YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated 
                           list of numbers and/or strings.''')
            ]
        )
    )
    return response.text

def google_search(query: str) -> str:
    '''Performs a web search for the given query using DuckDuckGo, and falls back to Wikipedia if no results are found.'''
    try:
        # Add delay between requests to avoid rate limiting
        time.sleep(1)
        
        response = requests.get(
            "https://api.duckduckgo.com/",
            params={
                "q": query,
                "format": "json",
                "no_html": 1,
                "no_redirect": 1,
                "skip_disambig": 1  # Skip disambiguation pages
            },
            timeout=10  # Add timeout
        )
        response.raise_for_status()
        data = response.json()
        
        # Enhanced result processing
        result = []
        
        # Add Abstract if available
        if data.get("Abstract"):
            result.append(data["Abstract"])
            
        # Add Definition if available
        if data.get("Definition"):
            result.append(f"Definition: {data['Definition']}")
            
        # Process Related Topics more thoroughly
        if data.get("RelatedTopics"):
            for topic in data["RelatedTopics"][:5]:  # Increased to 5 related topics
                if "Text" in topic:
                    result.append(topic["Text"])
                elif "Topics" in topic:
                    for subtopic in topic["Topics"][:2]:
                        if "Text" in subtopic:
                            result.append(subtopic["Text"])
        
        # Add Answer if available
        if data.get("Answer"):
            result.append(f"Answer: {data['Answer']}")
            
        # If no results found, try alternative search approach
        if not result:
            # Try searching with quotes for exact match
            quoted_response = requests.get(
                "https://api.duckduckgo.com/",
                params={
                    "q": f'"{query}"',
                    "format": "json",
                    "no_html": 1,
                    "no_redirect": 1
                },
                timeout=10
            )
            quoted_data = quoted_response.json()
            
            if quoted_data.get("Abstract"):
                result.append(quoted_data["Abstract"])
            if quoted_data.get("Answer"):
                result.append(f"Answer: {quoted_data['Answer']}")
        
        # If still no results, try Wikipedia as a fallback
        if not result:
            try:
                wiki_response = requests.get(
                    "https://en.wikipedia.org/w/api.php",
                    params={
                        "action": "query",
                        "format": "json",
                        "prop": "extracts",
                        "exintro": True,
                        "explaintext": True,
                        "titles": query
                    },
                    timeout=10
                )
                wiki_data = wiki_response.json()
                pages = wiki_data.get("query", {}).get("pages", {})
                for page in pages.values():
                    extract = page.get("extract")
                    if extract:
                        result.append(extract)
            except Exception as e:
                logger.error(f"Wikipedia fallback error for query {query}: {str(e)}")
        
        return "\n".join(result) if result else "No results found."
        
    except requests.exceptions.Timeout:
        logger.warning(f"Search timeout for query: {query}")
        return "Search timed out. Please try again."
    except requests.exceptions.RequestException as e:
        logger.error(f"Search error for query {query}: {str(e)}")
        return f"Error performing search: {str(e)}"
    except Exception as e:
        logger.error(f"Unexpected error during search for query {query}: {str(e)}")
        return "An unexpected error occurred during the search."

def log_message(message: BaseMessage, prefix: str = ""):
    """Helper function to log message details."""
    if isinstance(message, HumanMessage):
        logger.info(f"{prefix}Human Message:")
        if isinstance(message.content, list):
            for item in message.content:
                if isinstance(item, dict):
                    if item.get("type") == "media":
                        logger.info(f"{prefix}  Media content (type: {item.get('mime_type')})")
                    else:
                        logger.info(f"{prefix}  {item.get('type')}: {item.get('text')}")
                else:
                    logger.info(f"{prefix}  {item}")
        else:
            logger.info(f"{prefix}  {message.content}")
    elif isinstance(message, AIMessage):
        logger.info(f"{prefix}AI Message:")
        logger.info(f"{prefix}  Content: {message.content}")
        if hasattr(message, 'tool_calls') and message.tool_calls:
            logger.info(f"{prefix}  Tool Calls: {json.dumps(message.tool_calls, indent=2)}")
    elif isinstance(message, SystemMessage):
        logger.info(f"{prefix}System Message:")
        logger.info(f"{prefix}  {message.content}")

class BasicAgent:
    def __init__(self):
        # Initialize primary LLM (OpenAI)
        if os.getenv("OPENAI_API_KEY"):
            from langchain_openai import ChatOpenAI
            self.primary_llm = ChatOpenAI(
                model="gpt-3.5-turbo",
                temperature=0,
                max_tokens=4096
            )
        else:
            self.primary_llm = None
            
        # Initialize fallback LLM (Gemini)
        self.fallback_llm = ChatGoogleGenerativeAI(
            model="gemini-2.5-flash-preview-05-20",
            max_tokens=8192,
            temperature=0,
            convert_system_message_to_human=True
        )
        
        # Create tool executor
        self.tools = {
            "get_file": get_file,
            "analyse_excel": analyse_excel,
            "add_numbers": add_numbers,
            "transcribe_audio": transcribe_audio,
            "python_code": python_code,
            "open_image": open_image,
            "open_youtube_video": open_youtube_video,
            "google_search": google_search
        }
        
        # Define tool configurations
        self.openai_tools = [{
            "type": "function",
            "function": {
                "name": "google_search",
                "description": "Search for information on the web. Use this tool to find specific information about the question.",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "query": {
                            "type": "string",
                            "description": "The search query to find relevant information"
                        }
                    },
                    "required": ["query"]
                }
            }
        }]
        
        self.gemini_tools = [{
            "function_declarations": [{
                "name": "google_search",
                "description": "Search for information on the web. Use this tool to find specific information about the question.",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "query": {
                            "type": "string",
                            "description": "The search query to find relevant information"
                        }
                    },
                    "required": ["query"]
                }
            }]
        }]
        
        # System message with explicit tool usage instructions
        self.sys_msg = SystemMessage('''You are a general AI assistant. I will ask you a question. Follow these steps:

        1. First, use the google_search tool to find relevant information about the question.
        2. Analyze the search results to find the specific information needed.
        3. If needed, use additional tools to gather more information.
        4. Only after gathering all necessary information, provide YOUR FINAL ANSWER.

        YOUR FINAL ANSWER must be:
        - For numbers: Just the digit (e.g., "7" not "seven" or "7 albums")
        - For strings: As few words as possible
        - For lists: A comma-separated list of numbers and/or strings

        Rules for formatting:
        - For numbers: Don't use commas or units ($, %, etc.) unless specified
        - For strings: Don't use articles or abbreviations
        - For lists: Apply the above rules based on whether each element is a number or string

        IMPORTANT:
        - You MUST use the google_search tool before providing your final answer
        - Format your tool calls as: {"name": "google_search", "arguments": {"query": "your search query"}}
        - Your final answer should ONLY be the requested information, no explanations
        - If you need to search again, use the tool again
        - Do not provide detailed analysis in your final answer
        - If you encounter rate limits, inform the user that you need to search for information
        - Never make up information - if you can't find it, say so''')

        # Create the graph
        self.workflow = StateGraph(AgentState)
        
        # Add nodes
        self.workflow.add_node("agent", self.call_model)
        self.workflow.add_node("tools", self.call_tools)
        
        # Add edges
        self.workflow.add_edge("agent", "tools")
        self.workflow.add_edge("tools", "agent")
        
        # Set entry point
        self.workflow.set_entry_point("agent")
        
        # Compile the graph
        self.app = self.workflow.compile()
        
        # Set recursion limit through environment variable
        os.environ["LANGRAPH_RECURSION_LIMIT"] = "50"
        
        logger.info("BasicAgent initialized with fallback LLM support.")
    
    def _call_model_with_retry(self, state: AgentState) -> AgentState:
        """Internal method to handle retries for model calls."""
        max_retries = 3
        retry_count = 0
        last_error = None
        
        while retry_count < max_retries:
            try:
                messages = state["messages"]
                logger.info("\n=== Model Input ===")
                log_message(self.sys_msg, "  ")
                for msg in messages:
                    log_message(msg, "  ")
                
                # Try primary LLM first (OpenAI)
                try:
                    if self.primary_llm is None:
                        raise ValueError("Primary LLM not initialized")
                        
                    logger.info("Attempting to use primary LLM (OpenAI)")
                    
                    # Add explicit tool usage prompt
                    messages_with_tool_prompt = [self.sys_msg] + messages + [
                        HumanMessage(content="Use the google_search tool to find the information. Format your response as a JSON object with 'name' and 'arguments' fields.")
                    ]
                    
                    response = self.primary_llm.invoke(
                        messages_with_tool_prompt,
                        tools=self.openai_tools
                    )
                    
                    if not response or not hasattr(response, 'content'):
                        raise ValueError("Invalid response format from OpenAI")
                        
                    # Check if response contains tool call
                    if hasattr(response, 'tool_calls') and response.tool_calls:
                        logger.info("Successfully used primary LLM with tools")
                        return {"messages": [response], "next": "tools"}
                    else:
                        # If no tool call, try without tools
                        response = self.primary_llm.invoke(messages_with_tool_prompt)
                        if not response or not hasattr(response, 'content'):
                            raise ValueError("Invalid response format from OpenAI")
                        logger.info("Successfully used primary LLM without tools")
                        
                except Exception as e:
                    error_str = str(e)
                    logger.error(f"Primary LLM error: {error_str}")
                    
                    # Try fallback LLM (Gemini)
                    try:
                        logger.info("Attempting to use fallback LLM (Gemini)")
                        # Convert system message to human message for Gemini
                        if isinstance(self.sys_msg, SystemMessage):
                            system_content = f"System Instructions: {self.sys_msg.content}"
                            messages_with_system = [HumanMessage(content=system_content)] + messages
                        else:
                            messages_with_system = [self.sys_msg] + messages
                        
                        # Add explicit tool usage prompt
                        messages_with_tool_prompt = messages_with_system + [
                            HumanMessage(content="Use the google_search tool to find the information. Format your response as a JSON object with 'name' and 'arguments' fields.")
                        ]
                        
                        response = self.fallback_llm.invoke(
                            messages_with_tool_prompt,
                            tools=self.gemini_tools
                        )
                        
                        if not response or not hasattr(response, 'content'):
                            raise ValueError("Invalid response format from Gemini")
                            
                        # Check if response contains tool call
                        if hasattr(response, 'tool_calls') and response.tool_calls:
                            logger.info("Successfully used fallback LLM with tools")
                            return {"messages": [response], "next": "tools"}
                        else:
                            # If no tool call, try without tools
                            response = self.fallback_llm.invoke(messages_with_tool_prompt)
                            if not response or not hasattr(response, 'content'):
                                raise ValueError("Invalid response format from Gemini")
                            logger.info("Successfully used fallback LLM without tools")
                            
                    except Exception as fallback_error:
                        logger.error(f"Fallback LLM error: {str(fallback_error)}")
                        if "429" in str(fallback_error):
                            return {
                                "messages": [AIMessage(content="All LLM services are currently rate limited. Please try again later.")], 
                                "next": END
                            }
                        else:
                            return {
                                "messages": [AIMessage(content="All LLM services are currently unavailable. Please try again later.")], 
                                "next": END
                            }
                
                logger.info("\n=== Model Output ===")
                log_message(response, "  ")
                
                if not response or not response.content:
                    logger.error("Empty response from model")
                    raise ValueError("Empty response from model")
                
                # Process the response content
                content = response.content.strip()
                
                # If the model is just acknowledging or explaining, prompt it to use the tool
                if any(phrase in content.lower() for phrase in ["let me", "i'll", "i will", "sure", "okay", "alright"]):
                    logger.info("Model provided acknowledgment instead of tool call, prompting for search")
                    return {
                        "messages": [AIMessage(content="Please use the google_search tool to find the information.")],
                        "next": "agent"
                    }
                
                # Clean up the content to ensure it's in the correct format
                if content.startswith("**Final Answer**: "):
                    content = content.replace("**Final Answer**: ", "").strip()
                
                # For numbers, ensure they're in the correct format
                if content.replace(".", "").isdigit():
                    # Remove any decimal places for whole numbers
                    if float(content).is_integer():
                        content = str(int(float(content)))
                
                # Check if the content is a valid final answer
                if content.isdigit() or (content.startswith('[') and content.endswith(']')):
                    return {"messages": [AIMessage(content=content)], "next": END}
                else:
                    # If not a final answer, continue the conversation
                    return {"messages": [response], "next": "agent"}
                    
            except Exception as e:
                last_error = e
                retry_count += 1
                logger.error(f"Error in processing, retry {retry_count}/{max_retries}: {str(e)}")
                if retry_count < max_retries:
                    wait_time = 5 * retry_count  # Simple backoff
                    time.sleep(wait_time)
                else:
                    logger.error(f"All retries failed. Last error: {str(last_error)}")
                    return {
                        "messages": [AIMessage(content="Unable to generate answer after multiple attempts. Please try again later.")],
                        "next": END
                    }
        
        return {
            "messages": [AIMessage(content="Unable to generate answer after multiple attempts. Please try again later.")],
            "next": END
        }

    def call_model(self, state: AgentState) -> AgentState:
        """Call the model to generate a response with retry logic and fallback support."""
        return self._call_model_with_retry(state)
    
    def call_tools(self, state: AgentState) -> AgentState:
        """Call the tools based on the model's response."""
        try:
            messages = state["messages"]
            last_message = messages[-1]
            
            logger.info("\n=== Tool Execution ===")
            if isinstance(last_message, AIMessage):
                # Try to parse tool call from content if it's in JSON format
                content = last_message.content.strip()
                try:
                    if content.startswith('{') and content.endswith('}'):
                        tool_call = json.loads(content)
                        if isinstance(tool_call, dict) and 'name' in tool_call and 'arguments' in tool_call:
                            tool_name = tool_call['name']
                            tool_args = tool_call['arguments']
                            logger.info(f"Executing tool: {tool_name}")
                            logger.info(f"Tool arguments: {json.dumps(tool_args, indent=2)}")
                            
                            result = execute_tool(tool_name, tool_args, self.tools)
                            logger.info(f"Tool result: {result}")
                            
                            # Add tool result to messages
                            messages.append(AIMessage(content=f"Tool result: {result}"))
                            
                            # Loop breaker: If the result is 'No results found.', end with 'Not found'
                            if isinstance(result, str) and "no results found" in result.lower():
                                return {"messages": [AIMessage(content="Not found")], "next": END}
                            
                            # If this was a google_search, analyze the results
                            if tool_name == "google_search":
                                # Add a prompt to analyze the search results
                                messages.append(HumanMessage(content="Based on the search results, please provide your final answer."))
                            return {"messages": messages, "next": "agent"}
                except json.JSONDecodeError:
                    pass  # Not a JSON tool call, continue with normal processing
                
                # If no tool calls found, check if we need to prompt for a tool call
                content = last_message.content.strip().lower()
                if any(phrase in content for phrase in ["let me", "i'll", "i will", "sure", "okay", "alright"]):
                    logger.info("No tool calls found, prompting for search")
                    messages.append(AIMessage(content="Please use the google_search tool to find the information."))
                else:
                    logger.info("No tool calls found in AI message")
                    # If the message looks like a final answer, return it
                    if content.isdigit() or (content.startswith('[') and content.endswith(']')):
                        return {"messages": [last_message], "next": END}
                    else:
                        # Otherwise, continue the conversation
                        return {"messages": messages, "next": "agent"}
            
            return {"messages": messages, "next": "agent"}
        except Exception as e:
            logger.error(f"Error in call_tools: {str(e)}")
            # If there's an error, try to continue the conversation
            return {"messages": messages, "next": "agent"}
    
    async def __call__(self, question: str, task_id: str) -> str:
        """Process a question and return the answer with error handling."""
        logger.info(f"\n=== Processing Question ===")
        logger.info(f"Task ID: {task_id}")
        logger.info(f"Question: {question}")
        
        try:
            # Create initial state
            initial_state = {
                "messages": [HumanMessage(content=f'Task id: {task_id}\n {question}')],
                "next": "agent"
            }
            
            # Process through the graph with retry logic
            result = self.app.invoke(initial_state)
            final_message = result["messages"][-1]
            
            if isinstance(final_message, AIMessage) and final_message.content:
                logger.info(f"\n=== Final Answer ===")
                logger.info(f"Answer: {final_message.content}")
                return final_message.content
            else:
                logger.error("Empty or invalid response")
                raise ValueError("Empty or invalid response")
                
        except Exception as e:
            logger.error(f"Fatal error in agent: {str(e)}")
            return f"Error: {str(e)}"

def run_and_submit_all(profile):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code

    if profile:
        username = str(profile)
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        agent = BasicAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
             print("Fetched questions list is empty.")
             return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except requests.exceptions.JSONDecodeError as e:
         print(f"Error decoding JSON response from questions endpoint: {e}")
         print(f"Response text: {response.text[:500]}")
         return f"Error decoding server response for questions: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run your Agent
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        try:
            submitted_answer = asyncio.run(agent(question_text, task_id))
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
        except Exception as e:
             print(f"Error running agent on task {task_id}: {e}")
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})

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

    # 4. Prepare Submission 
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    # 5. Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        print("Submission successful.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_json = e.response.json()
            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        status_message = f"Submission Failed: {error_detail}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
        ---
        **Disclaimers:**
        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
        """
    )

    with gr.Row():
        login_button = gr.LoginButton()
        run_button = gr.Button("Run Evaluation & Submit All Answers")  # always enabled

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    def run_evaluation(profile):
        if not profile:
            return "Please login first.", None
        return run_and_submit_all(profile)

    run_button.click(
        fn=run_evaluation,
        inputs=[login_button],
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    # Check for SPACE_HOST and SPACE_ID at startup for information
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup: # Print repo URLs if SPACE_ID is found
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

    print("-"*(60 + len(" App Starting ")) + "\n")

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