app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import time
import json
from typing import Dict, List, Union, Optional
import re
from bs4 import BeautifulSoup
from duckduckgo_search import DDGS

from smolagents import Tool, CodeAgent, InferenceClientModel

import random
from smolagents import CodeAgent, InferenceClientModel

# Import our custom tools from their modules
# from smolagents.tools import DuckDuckGoSearchTool, WeatherInfoTool, HubStatsTool
# from smolagents.tools import WebPageVisitTool, WebpageContentExtractorTool

from smolagents import CodeAgent, InferenceClientModel, load_tool


# Import necessary libraries
import random
from smolagents import CodeAgent, InferenceClientModel

# Import our custom tools from their modules
# from tools import DuckDuckGoSearchTool, WeatherInfoTool, HubStatsTool
# from retriever import load_guest_dataset

from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.retrievers import BM25Retriever
import functools

# Create a knowledge base for the agent
GAIA_KNOWLEDGE = """
### AI and Agent Concepts
- An agent is an autonomous entity that observes and acts upon an environment using sensors and actuators, usually to achieve specific goals.
- GAIA (General AI Assistant) is a framework for creating and evaluating AI assistants that can perform a wide range of tasks.
- The agent loop consists of perception, reasoning, and action.
- RAG (Retrieval-Augmented Generation) combines retrieval of relevant information with generation capabilities of language models.
- An LLM (Large Language Model) is a neural network trained on vast amounts of text data to understand and generate human language.

### Agent Capabilities
- Tool use refers to an agent's ability to employ external tools like search engines, APIs, or specialized algorithms.
- An effective agent should be able to decompose complex problems into manageable parts.
- Chain-of-thought reasoning allows agents to break down problem-solving steps to improve accuracy.
- Agents should apply appropriate reasoning strategies based on the type of question (factual, analytical, etc.)
- Self-reflection helps agents identify and correct errors in their reasoning.

### Evaluation Criteria
- Agent responses should be accurate, relevant, and factually correct.
- Effective agents provide concise yet comprehensive answers.
- Agents should acknowledge limitations and uncertainties when appropriate.
- Good agents can follow multi-step instructions and fulfill all requirements.
- Reasoning transparency helps users understand how the agent arrived at its conclusions.
"""

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

LLAMA_API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-3.1-8B-Instruct"
HF_API_TOKEN = os.getenv("HF_API_TOKEN")
HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"} if HF_API_TOKEN else {}
MAX_RETRIES = 3
RETRY_DELAY = 2  # seconds

# Create knowledge base documents
def create_knowledge_documents():
    """Create documents from the knowledge base for retrieval."""
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=500,
        chunk_overlap=50,
        separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""]
    )
    knowledge_chunks = text_splitter.split_text(GAIA_KNOWLEDGE)
    return [Document(page_content=chunk) for chunk in knowledge_chunks]

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------

# --- Tools ---
class WebSearchTool(Tool):
    name = "web_search"
    description = "Search the web for information about a query using DuckDuckGo."
    inputs = {
        "query": {
            "type": "string",
            "description": "The search query."
        }
    }
    output_type = "string"
    
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.max_results = 3
        
    def forward(self, query: str) -> str:
        assert isinstance(query, str), "Query must be a string."
        try:
            results = []
            with DDGS() as ddgs:
                ddgs_results = list(ddgs.text(query, max_results=self.max_results))
                
            if not ddgs_results:
                return "No web search results found."
                
            formatted_results = "\nWeb Search Results:\n"
            for i, r in enumerate(ddgs_results, 1):
                formatted_results += f"\n{i}. {r['title']}\n   {r['body']}\n   Source: {r['href']}\n"
                
            return formatted_results
        except Exception as e:
            print(f"Error in web search: {str(e)}")
            return f"Error performing web search: {str(e)}"


class WebContentTool(Tool):
    name = "web_content"
    description = "Fetch and extract content from a specific webpage."
    inputs = {
        "url": {
            "type": "string",
            "description": "The URL of the webpage to fetch content from."
        }
    }
    output_type = "string"
    
    def forward(self, url: str) -> str:
        assert isinstance(url, str), "URL must be a string."
        try:
            headers = {
                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
            }
            response = requests.get(url, headers=headers, timeout=10)
            response.raise_for_status()
            
            soup = BeautifulSoup(response.text, 'html.parser')
            
            # Remove script and style elements
            for script in soup(["script", "style"]):
                script.extract()
                
            # Get text content
            text = soup.get_text(separator='\n')
            
            # Clean up text (remove extra whitespace and blank lines)
            lines = (line.strip() for line in text.splitlines())
            chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
            text = '\n'.join(chunk for chunk in chunks if chunk)
            
            # Truncate if too long
            if len(text) > 2000:
                text = text[:2000] + "... [content truncated]"
                
            return f"Content from {url}:\n\n{text}"
        except Exception as e:
            print(f"Error fetching web content: {str(e)}")
            return f"Error fetching content from {url}: {str(e)}"


class GaiaRetrieverTool(Tool):
    name = "gaia_retriever"
    description = "Semantic search for retrieving relevant information for GaiaAgent."
    inputs = {
        "query": {
            "type": "string",
            "description": "Query for semantic search."
        }
    }
    output_type = "string"

    def __init__(self, docs, **kwargs):
        super().__init__(**kwargs)
        self.retriever = BM25Retriever.from_documents(docs, k=3)
        self.docs = docs  # Store docs for fallback

    def forward(self, query: str) -> str:
        assert isinstance(query, str), "Query must be a string."
        try:
            docs = self.retriever.invoke(query)
            if not docs:
                # Fallback to return most relevant general knowledge
                return "\nNo specific information found. Here's some general knowledge:\n" + "".join([
                    f"\n- {self.docs[i].page_content}" for i in range(min(3, len(self.docs)))
                ])
            return "\nRetrieved Information:\n" + "".join([
                f"\n- {doc.page_content}" for doc in docs
            ])
        except Exception as e:
            print(f"Error in retriever: {str(e)}")
            # Return a fallback response
            return f"Unable to retrieve specific information. The agent will rely on its general knowledge."

# --- Agent ---
class GaiaAgent:
    def __init__(self):
        print("GaiaAgent initialized.")
        # Create knowledge base documents
        self.knowledge_docs = create_knowledge_documents()
        
        # Create our tools
        self.retriever_tool = GaiaRetrieverTool(self.knowledge_docs)
        self.web_search_tool = WebSearchTool()
        self.web_content_tool = WebContentTool()

        # Initialize the Hugging Face model
        self.model = InferenceClientModel()

        # Initialize the web search tool
        # self.search_tool = DuckDuckGoSearchTool()

        # Initialize the weather tool
        # self.weather_info_tool = WeatherInfoTool()

        # Initialize the Hub stats tool
        # self.hub_stats_tool = HubStatsTool()

        # Load the guest dataset and initialize the guest info tool
        # self.guest_info_tool = load_guest_dataset()
        
        # Set up LLM API access
        self.hf_api_url = LLAMA_API_URL
        self.headers = HEADERS
        
        # Set up caching for responses
        self.cache = {}
        
    def query_llm(self, prompt):
        """Send a prompt to the LLM API and return the response."""
        # Check cache first
        if prompt in self.cache:
            print("Using cached response")
            return self.cache[prompt]
            
        if not HF_API_TOKEN:
            # Fallback to rule-based approach if no API token
            return self.rule_based_answer(prompt)
            
        payload = {
            "inputs": prompt,
            "parameters": {
                "max_new_tokens": 512,
                "temperature": 0.7,
                "top_p": 0.9,
                "do_sample": True
            }
        }
        
        for attempt in range(MAX_RETRIES):
            try:
                response = requests.post(self.hf_api_url, headers=self.headers, json=payload, timeout=30)
                response.raise_for_status()
                result = response.json()
                
                # Extract the generated text from the response
                if isinstance(result, list) and len(result) > 0:
                    generated_text = result[0].get("generated_text", "")
                    # Clean up the response to get just the answer
                    clean_response = self.clean_response(generated_text, prompt)
                    # Cache the response
                    self.cache[prompt] = clean_response
                    return clean_response
                return "I couldn't generate a proper response."
                
            except Exception as e:
                print(f"Attempt {attempt+1}/{MAX_RETRIES} failed: {str(e)}")
                if attempt < MAX_RETRIES - 1:
                    time.sleep(RETRY_DELAY)
                else:
                    # Fall back to rule-based method on failure
                    return self.rule_based_answer(prompt)
    
    def clean_response(self, response, prompt):
        """Clean up the LLM response to extract the answer."""
        # Remove the prompt from the beginning if it's included
        if response.startswith(prompt):
            response = response[len(prompt):]
        
        # Try to find where the model's actual answer begins
        markers = ["<answer>", "<response>", "Answer:", "Response:", "Assistant:"]
        for marker in markers:
            if marker.lower() in response.lower():
                parts = response.lower().split(marker.lower(), 1)
                if len(parts) > 1:
                    response = parts[1].strip()
        
        # Remove any closing tags if they exist
        end_markers = ["</answer>", "</response>", "Human:", "User:"]
        for marker in end_markers:
            if marker.lower() in response.lower():
                response = response.lower().split(marker.lower())[0].strip()
        
        return response.strip()
    
    def rule_based_answer(self, question):
        """Fallback method using rule-based answers for common question types."""
        question_lower = question.lower()
        
        # Simple pattern matching for common question types
        if "what is" in question_lower or "define" in question_lower:
            if "agent" in question_lower:
                return "An agent is an autonomous entity that observes and acts upon an environment using sensors and actuators, usually to achieve specific goals."
            if "gaia" in question_lower:
                return "GAIA (General AI Assistant) is a framework for creating and evaluating AI assistants that can perform a wide range of tasks."
            if "llm" in question_lower or "large language model" in question_lower:
                return "A Large Language Model (LLM) is a neural network trained on vast amounts of text data to understand and generate human language."
            if "rag" in question_lower or "retrieval" in question_lower:
                return "RAG (Retrieval-Augmented Generation) combines retrieval of relevant information with generation capabilities of language models."
        
        if "how to" in question_lower:
            return "To accomplish this task, you should first understand the requirements, then implement a solution step by step, and finally test your implementation."
        
        if "example" in question_lower:
            return "Here's an example implementation that demonstrates the concept in a practical manner."
        
        if "evaluate" in question_lower or "criteria" in question_lower:
            return "Evaluation criteria for agents typically include accuracy, relevance, factual correctness, conciseness, ability to follow instructions, and transparency in reasoning."
        
        # Default response for unmatched questions
        return "Based on my understanding, the answer involves analyzing the context carefully and applying the relevant principles to arrive at a solution."

    def determine_tools_needed(self, question):
        """Determine which tools should be used for a given question."""
        question_lower = question.lower()
        
        # Patterns that suggest the need for web search
        web_search_patterns = [
            "current", "latest", "recent", "news", "update", "today", 
            "statistics", "data", "facts", "information about",
            "what is happening", "how many", "where is", "when was"
        ]
        
        # Check if the question likely needs web search
        needs_web_search = False
        for pattern in web_search_patterns:
            if pattern in question_lower:
                needs_web_search = True
                break
                
        # Check if question appears to be about GAIA, agents, or AI concepts
        needs_knowledge_retrieval = any(term in question_lower for term in 
                                       ["agent", "gaia", "llm", "ai", "artificial intelligence", 
                                        "evaluation", "tool", "rag", "retrieval"])
        
        # Determine which tools to use based on the analysis
        return {
            "use_web_search": needs_web_search,
            "use_knowledge_retrieval": needs_knowledge_retrieval or not needs_web_search,  # Fallback to knowledge retrieval
            "use_webpage_visit": "example" in question_lower or "details" in question_lower or "explain" in question_lower
        }
    
    def format_prompt(self, question, knowledge_info="", web_info="", webpage_content=""):
        """Format the question into a proper prompt for the LLM."""
        context = ""
        
        if knowledge_info:
            context += f"\nLocal Knowledge Base Information:\n{knowledge_info}\n"
            
        if web_info:
            context += f"\nWeb Search Results:\n{web_info}\n"
            
        if webpage_content:
            context += f"\nDetailed Web Content:\n{webpage_content}\n"
            
        if context:
            return f"""You are an intelligent AI assistant specialized in answering questions about AI agents, GAIA (General AI Assistant), and related concepts.
Use the following information to help answer the question accurately. If the information doesn't contain what you need, use your general knowledge.

{context}

Question: {question}

Provide a clear, concise, and accurate answer. Use reasoning steps when appropriate. If you're uncertain, acknowledge limitations.

Answer:"""
        else:
            return f"""You are an intelligent AI assistant specialized in answering questions about AI agents, GAIA (General AI Assistant), and related concepts.

Question: {question}

Provide a clear, concise, and accurate answer. Use reasoning steps when appropriate. If you're uncertain, acknowledge limitations.

Answer:"""
    
    def __call__(self, question: str) -> str:
        print(f"GaiaAgent received question (first 50 chars): {question[:50]}...")
        
        try:
            # Step 1: Determine which tools to use
            tool_selection = self.determine_tools_needed(question)
            
            # Step 2: Gather information from selected tools
            knowledge_info = ""
            web_info = ""
            webpage_content = ""
            
            # Get knowledge base information
            if tool_selection["use_knowledge_retrieval"]:
                try:
                    knowledge_info = self.retriever_tool.forward(question)
                    print("Retrieved knowledge base information")
                except Exception as e:
                    print(f"Error retrieving knowledge base information: {e}")
                
            # Get web search results
            if tool_selection["use_web_search"]:
                try:
                    web_info = self.web_search_tool.forward(question)
                    print("Retrieved web search results")
                except Exception as e:
                    print(f"Error with web search: {e}")
                
                # If web search found URLs and we should visit them
                if tool_selection["use_webpage_visit"] and web_info and "http" in web_info.lower():
                    # Extract URL from search results
                    url_match = re.search(r'Source: (https?://[^\s]+)', web_info)
                    if url_match:
                        url = url_match.group(1)
                        try:
                            content_result = self.web_content_tool.forward(url)
                            
                            # Only use if result seems valid
                            if content_result and len(content_result) > 100:
                                webpage_content = content_result
                                print(f"Retrieved webpage content from {url}")
                            else:
                                print("Webpage content was too short or empty")
                                
                        except Exception as e:
                            print(f"Error extracting webpage content: {e}")
            
            # Step 3: Format prompt with gathered information
            prompt = self.format_prompt(question, knowledge_info, web_info, webpage_content)
            
            # Step 4: Query the LLM with the formatted prompt
            answer = self.query_llm(prompt)
            
            print(f"GaiaAgent returning answer (first 50 chars): {answer[:50]}...")
            return answer
            
        except Exception as e:
            print(f"Error in GaiaAgent: {e}")
            # Fallback to the rule-based method if anything goes wrong
            fallback_answer = self.rule_based_answer(question)
            print(f"GaiaAgent returning fallback answer: {fallback_answer[:50]}...")
            return fallback_answer
    
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        # Initialize the Hugging Face API client
        # https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct
        self.hf_api_url = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
        self.hf_api_token = os.getenv("HF_API_TOKEN")
        if not self.hf_api_token:
            print("WARNING: HF_API_TOKEN not found. Using default fallback methods.")
        self.headers = {"Authorization": f"Bearer {self.hf_api_token}"} if self.hf_api_token else {}
        self.max_retries = 3
        self.retry_delay = 2  # seconds
        
    def query_llm(self, prompt):
        """Send a prompt to the LLM API and return the response."""
        if not self.hf_api_token:
            # Fallback to a rule-based approach if no API token
            return self.rule_based_answer(prompt)
            
        payload = {
            "inputs": prompt,
            "parameters": {
                "max_new_tokens": 512,
                "temperature": 0.7,
                "top_p": 0.9,
                "do_sample": True
            }
        }
        
        for attempt in range(self.max_retries):
            try:
                response = requests.post(self.hf_api_url, headers=self.headers, json=payload, timeout=30)
                response.raise_for_status()
                result = response.json()
                
                # Extract the generated text from the response
                if isinstance(result, list) and len(result) > 0:
                    generated_text = result[0].get("generated_text", "")
                    # Clean up the response to get just the answer
                    return self.clean_response(generated_text, prompt)
                return "I couldn't generate a proper response."
                
            except Exception as e:
                print(f"Attempt {attempt+1}/{self.max_retries} failed: {str(e)}")
                if attempt < self.max_retries - 1:
                    time.sleep(self.retry_delay)
                else:
                    # Fall back to rule-based method on failure
                    return self.rule_based_answer(prompt)
    
    def clean_response(self, response, prompt):
        """Clean up the LLM response to extract the answer."""
        # Remove the prompt from the beginning if it's included
        if response.startswith(prompt):
            response = response[len(prompt):]
        
        # Try to find where the model's actual answer begins
        # This is model-specific and may need adjustments
        markers = ["<answer>", "<response>", "Answer:", "Response:"]
        for marker in markers:
            if marker.lower() in response.lower():
                parts = response.lower().split(marker.lower(), 1)
                if len(parts) > 1:
                    response = parts[1].strip()
        
        # Remove any closing tags if they exist
        end_markers = ["</answer>", "</response>"]
        for marker in end_markers:
            if marker.lower() in response.lower():
                response = response.lower().split(marker.lower())[0].strip()
        
        return response.strip()
    
    def rule_based_answer(self, question):
        """Fallback method using rule-based answers for common question types."""
        question_lower = question.lower()
        
        # Simple pattern matching for common question types
        if "what is" in question_lower or "define" in question_lower:
            if "agent" in question_lower:
                return "An agent is an autonomous entity that observes and acts upon an environment using sensors and actuators, usually to achieve specific goals."
            if "gaia" in question_lower:
                return "GAIA (General AI Assistant) is a framework for creating and evaluating AI assistants that can perform a wide range of tasks."
        
        if "how to" in question_lower:
            return "To accomplish this task, you should first understand the requirements, then implement a solution step by step, and finally test your implementation."
        
        if "example" in question_lower:
            return "Here's an example implementation that demonstrates the concept in a practical manner."
        
        # Default response for unmatched questions
        return "Based on my understanding, the answer involves analyzing the context carefully and applying the relevant principles to arrive at a solution."
    
    def format_prompt(self, question):
        """Format the question into a proper prompt for the LLM."""
        return f"""You are an intelligent AI assistant. Please answer the following question accurately and concisely:

Question: {question}

Answer:"""
    
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        
        try:
            # Format the question as a prompt
            prompt = self.format_prompt(question)
            
            # Query the LLM
            answer = self.query_llm(prompt)
            
            print(f"Agent returning answer (first 50 chars): {answer[:50]}...")
            return answer
            
        except Exception as e:
            print(f"Error in agent: {e}")
            # Fallback to the rule-based method if anything goes wrong
            fallback_answer = self.rule_based_answer(question)
            print(f"Agent returning fallback answer: {fallback_answer[:50]}...")
            return fallback_answer

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

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        print("Initializing GaiaAgent...")
        agent = GaiaAgent()

        # Initialize the Hugging Face model
        model = InferenceClientModel()

        # Initialize the web search tool
        #search_tool = DuckDuckGoSearchTool()

        # Initialize the weather tool
        #weather_info_tool = WeatherInfoTool()

        # Initialize the Hub stats tool
        #hub_stats_tool = HubStatsTool()

        # Load the guest dataset and initialize the guest info tool
        guest_info_tool = load_guest_dataset()

        # Initialize the Hugging Face model
        model = InferenceClientModel()

        # Load the DuckDuckGo search tool dynamically
        search_tool = load_tool(repo_id="smol-ai/duckduckgo-search", trust_remote_code=True)


        agent = CodeAgent(
            tools=[guest_info_tool, search_tool], 
            model=model,
            add_base_tools=True,  # Add any additional base tools
            planning_interval=3   # Enable planning every 3 steps
        )
        
        print("GaiaAgent initialization complete.")
    except Exception as e:
        print(f"Error instantiating GaiaAgent: {e}")
        print("Falling back to BasicAgent...")
        try:
            agent = BasicAgent()
            print("BasicAgent initialization complete.")
        except Exception as e:
            print(f"Error instantiating BasicAgent: {e}")
            return f"Error initializing agents: {e}", None
    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

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

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

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

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

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


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# 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.
        """
    )

    gr.LoginButton()

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

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    # Removed max_rows=10 from DataFrame constructor
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

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

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

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

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

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