app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from smolagents import tool, Tool, CodeAgent, DuckDuckGoSearchTool, HfApiModel, VisitWebpageTool, SpeechToTextTool, FinalAnswerTool
from dotenv import load_dotenv
import heapq
from collections import Counter
import re
from io import BytesIO
from youtube_transcript_api import YouTubeTranscriptApi
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_community.document_loaders import ArxivLoader


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

#Load environment variables
load_dotenv()


import os
import time
import json
from typing import TypedDict, List, Union, Any, Dict, Optional

# LangChain and LangGraph imports
from langchain.schema import HumanMessage, AIMessage, SystemMessage
from langchain.prompts import ChatPromptTemplate
from langgraph.graph import StateGraph, END
from langchain_community.llms import HuggingFacePipeline 
from typing import List, Union, Dict, Any, TypedDict # Ensure all types are imported

import torch
from langchain_core.messages import AIMessage, HumanMessage # Corrected import for message types
from langchain_core.tools import BaseTool
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.documents import Document
from langchain_community.llms import HuggingFacePipeline
from langchain.prompts import ChatPromptTemplate # SystemMessage moved to langchain_core.messages
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from langgraph.graph import END, StateGraph

# Corrected Tool import: Use 'tool' (lowercase)
from langchain_core.tools import BaseTool, tool 

# Hugging Face local model imports
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch 

# Tool-specific imports
from duckduckgo_search import DDGS
import wikipedia
import arxiv
from transformers import pipeline as hf_pipeline # Renamed to avoid clash with main pipeline
from youtube_transcript_api import YouTubeTranscriptApi

# --- Helper function for python_execution tool ---
def indent_code(code: str, indent: str = "    ") -> str:
    """Indents multi-line code for execution within a function."""
    return "\n".join(indent + line for line in code.splitlines())

# --- Tool Definitions ---
@tool 
def duckduckgo_search(query: str) -> str:
    """Search web using DuckDuckGo. Returns top 3 results."""
    print(f"DEBUG: duckduckgo_search called with: {query}")
    try:
        with DDGS() as ddgs:
            return "\n\n".join(
                f"Title: {res['title']}\nURL: {res['href']}\nSnippet: {res['body']}"
                for res in ddgs.text(query, max_results=3)
            )
    except Exception as e:
        return f"Error performing DuckDuckGo search: {str(e)}"

@tool 
def wikipedia_search(query: str) -> str:
    """Get Wikipedia summaries. Returns first 3 sentences."""
    print(f"DEBUG: wikipedia_search called with: {query}")
    try:
        return wikipedia.summary(query, sentences=3)
    except wikipedia.DisambiguationError as e:
        return f"Disambiguation options: {', '.join(e.options[:3])}"
    except wikipedia.PageError:
        return "Wikipedia page not found."
    except Exception as e:
        return f"Error performing Wikipedia search: {str(e)}"

@tool 
def arxiv_search(query: str) -> str:
    """Search academic papers on arXiv. Returns top 3 results."""
    print(f"DEBUG: arxiv_search called with: {query}")
    try:
        results = arxiv.Search(
            query=query, 
            max_results=3,
            sort_by=arxiv.SortCriterion.Relevance
        ).results()
        
        return "\n\n".join(
            f"Title: {r.title}\nAuthors: {', '.join(a.name for a in r.authors)}\n"
            f"Published: {r.published.strftime('%Y-%m-%d')}\nSummary: {r.summary[:250]}..."
            for r in results
        )
    except Exception as e:
        return f"Error performing ArXiv search: {str(e)}"

@tool 
def document_qa(input_str: str) -> str:
    """Answer questions from documents. Input format: 'document_text||question'"""
    print(f"DEBUG: document_qa called with: {input_str}")
    try:
        if '||' not in input_str:
            return "Invalid format. Input must be: 'document_text||question'"
        
        context, question = input_str.split('||', 1)
        # Load QA model on first call or ensure it's loaded once globally.
        # It's better to load once in __init__ for BasicAgent if possible,
        # but this lazy loading prevents initial heavy load if tool is not used.
        qa_model = hf_pipeline('question-answering', model='deepset/roberta-base-squad2')
        return qa_model(question=question, context=context)['answer']
    except Exception as e:
        return f"Error answering question from document: {str(e)}"

@tool 
def python_execution(code: str) -> str:
    """Execute Python code and return output.
    The code should assign its final result to a variable named '_result_value'.
    Example: '_result_value = 1 + 1'
    """
    print(f"DEBUG: python_execution called with: {code}")
    try:
        # Create isolated environment
        env = {}
        # Wrap code in a function to isolate scope and capture '_result_value'
        # The exec function is used carefully here. In a production environment,
        # consider a more robust and secure sandbox (e.g., Docker, dedicated service).
        exec(f"def __exec_fn__():\n{indent_code(code)}\n_result_value = __exec_fn__()", globals(), env)
        return str(env.get('_result_value', 'No explicit result assigned to "_result_value" variable.'))
    except Exception as e:
        return f"Python execution error: {str(e)}"

class VideoTranscriptionTool(Tool):
    name: str = "transcript_video"
    # CORRECTED LINE BELOW: Added '=' for assignment
    description: str = "Fetch text transcript from YouTube videos using URL or ID. Use for any question involving video or audio. Input is the YouTube URL or ID." 

    def _run(self, url_or_id: str) -> str:
        print(f"DEBUG: transcript_video called with: {url_or_id}")
        video_id = None
        # Basic parsing for common YouTube URL formats
        if "youtube.com/watch?v=" in url_or_id:
            video_id = url_or_id.split("v=")[1].split("&")[0]
        elif "youtu.be/" in url_or_id:
            video_id = url_or_id.split("youtu.be/")[1].split("?")[0]
        elif len(url_or_id.strip()) == 11 and not ("http://" in url_or_id or "https://" in url_or_id):
            video_id = url_or_id.strip() # Assume it's just the ID

        if not video_id:
            return f"Invalid or unsupported YouTube URL/ID: {url_or_id}. Please provide a valid YouTube URL or 11-character ID."

        try:
            transcription = YouTubeTranscriptApi.get_transcript(video_id)
            return " ".join([part['text'] for part in transcription])

        except Exception as e:
            return f"Error fetching transcript for video ID '{video_id}': {str(e)}. It might not have an English transcript, or the video is unavailable."

    def _arun(self, *args, **kwargs):
        raise NotImplementedError("Async not supported for this tool.")


# ====== IMPORTS ======
import json
import time
from typing import Dict, List, Tuple, Any, Optional
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from langchain_core.prompts import ChatPromptTemplate
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.documents import Document

# ====== TYPE DEFINITIONS ======
AgentState = Dict[str, Any]

# ====== DOCUMENT PROCESSING ======
def create_vector_store() -> Optional[FAISS]:
    """Create vector store with predefined documents using FAISS"""
    try:
        # Define the documents
        documents = [
            Document(page_content="The capital of France is Paris.", metadata={"source": "geography"}),
            Document(page_content="Python is a popular programming language created by Guido van Rossum.", metadata={"source": "tech"}),
            Document(page_content="The Eiffel Tower is located in Paris, France.", metadata={"source": "landmarks"}),
        ]
        
        # Initialize embedding model
        embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
        
        # Split documents into chunks
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=500,
            chunk_overlap=100
        )
        chunks = text_splitter.split_documents(documents)
        
        # Create FAISS vector store
        return FAISS.from_documents(
            documents=chunks,
            embedding=embeddings
        )
    except Exception as e:
        print(f"ERROR creating vector store: {str(e)}")
        return None

# ====== AGENT HELPER FUNCTIONS ======
def parse_agent_response(content: str) -> Tuple[str, str, str]:
    """Parse the agent's JSON response"""
    try:
        # Extract JSON from content
        json_start = content.find('{')
        json_end = content.rfind('}') + 1
        json_str = content[json_start:json_end]
        
        # Parse JSON
        response_dict = json.loads(json_str)
        
        reasoning = response_dict.get("Reasoning", "No reasoning provided")
        action = response_dict.get("Action", "No action specified")
        action_input = response_dict.get("Action Input", "No input provided")
        
        return reasoning, action, action_input
    except json.JSONDecodeError:
        print(f"WARNING: Failed to parse JSON from response: {content[:200]}...")
        return "Failed to parse response", "Final Answer", "Error: Could not parse agent response"
    except Exception as e:
        print(f"ERROR parsing agent response: {str(e)}")
        return "Error in parsing", "Final Answer", f"Internal error: {str(e)}"

def should_continue(state: AgentState) -> str:
    """Determine if we should continue processing or end"""
    if state.get("final_answer"):
        return "end"
    if state.get("context", {}).get("pending_action"):
        return "action"
    return "reason"

# ====== REASONING NODE ======
def reasoning_node(state: AgentState) -> AgentState:
    """Node for analyzing questions and determining next steps"""
    print(f"DEBUG: Entering reasoning_node. Iteration: {state.get('iterations', 0)}")
    
    # Safely initialize state components
    state.setdefault("context", {})
    state.setdefault("reasoning", "")
    state.setdefault("iterations", 0)
    state.setdefault("current_task", "Understand the question and plan the next step.")
    state.setdefault("current_thoughts", "")
    state.setdefault("history", [])
    
    # Safely remove pending_action
    state["context"].pop("pending_action", None) 

    # Initialize local HuggingFacePipeline
    model_name = "mistralai/Mistral-7B-Instruct-v0.2"
    
    try:
        print(f"DEBUG: Loading local model: {model_name}...")
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        
        # Determine torch dtype based on available hardware
        if torch.cuda.is_available():
            torch_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
        else:
            torch_dtype = torch.float32
            
        model = AutoModelForCausalLM.from_pretrained(
            model_name, 
            torch_dtype=torch_dtype,
            device_map="auto"
        )

        # Create transformers pipeline
        pipe = pipeline(
            "text-generation",
            model=model,
            tokenizer=tokenizer,
            max_new_tokens=1024,
            temperature=0.1,
            do_sample=True,
            top_p=0.9,
            repetition_penalty=1.1,
        )

        llm = HuggingFacePipeline(pipeline=pipe)
    except Exception as e:
        print(f"ERROR loading model: {str(e)}")
        state["history"].append(AIMessage(content=f"[ERROR] Failed to load model: {str(e)}"))
        state["final_answer"] = "Error: Failed to initialize language model"
        return state

    # Prepare tool descriptions
    tool_descriptions = "\n".join([
        f"- **{t.name}**: {t.description}" for t in state.get("tools", [])
    ])

    # RAG Retrieval
    rag_context = ""
    vector_store = state["context"].get("vector_store")
    
    if vector_store:
        try:
            # Perform retrieval
            relevant_docs = vector_store.similarity_search(
                state.get("question", ""), 
                k=3
            )
            
            # Format context for LLM
            rag_context = "\n\n[Relevant Knowledge]\n"
            rag_context += "\n---\n".join([doc.page_content for doc in relevant_docs])
        except Exception as e:
            print(f"WARNING: RAG retrieval failed: {str(e)}")
            rag_context = "\n\n[Relevant Knowledge] Retrieval failed. Proceeding without additional context."
    else:
        print("WARNING: No vector store available for RAG")
        rag_context = "\n\n[Relevant Knowledge] No knowledge base available."

    # Renamed the variable to emphasize it's a template string, not a SystemMessage object
    system_prompt_template_str = ( 
        "You are an expert problem solver, designed to provide concise and accurate answers. "
        "Your process involves analyzing the question, intelligently selecting and using tools, "
        "and synthesizing information.\n\n"
        "**Available Tools:**\n"
        f"{tool_descriptions}\n\n"
        "**Tool Usage Guidelines:**\n"
        "- Use **duckduckgo_search** for current events, general facts, or quick lookups. Provide a concise search query. Example: `What is the population of New York?`\n"
        "- Use **wikipedia_search** for encyclopedic information, historical context, or detailed topics. Provide a concise search term. Example: `Eiffel Tower history`\n"
        "- Use **arxiv_search** for scientific papers, research, or cutting-edge technical information. Provide a concise search query. Example: `Large Language Models recent advances`\n"
        "- Use **document_qa** when the question explicitly refers to a specific document or when you have content to query. Input format: 'document_text||question'. Example: `The capital of France is Paris.||What is the capital of France?`\n"
        "- Use **python_execution** for complex calculations, data manipulation, or logical operations that cannot be done with simple reasoning. Always provide the full Python code, ensuring it's valid and executable, and assign the final result to a variable named '_result_value'. Example: `_result_value = 1 + 1`\n"
        "- Use **transcript_video** for any question involving video or audio content (e.g., YouTube). Provide the full YouTube URL or video ID. Example: `youtube.com`\n\n"
        "**Crucial Instructions:**\n"
        "1. **Always aim to provide a definitive answer.** If you have enough information, use the 'final answer' action.\n"
        "2. **To provide a final answer, use the Action 'final answer' with the complete answer in 'Action Input'.** This is how you tell me you're done. Example:\n"
        "   ```json\n"
        "   {\n"
        "     \"Reasoning\": \"I have found the capital of France.\",\n"
        "     \"Action\": \"final answer\",\n"
        "     \"Action Input\": \"The capital of France is Paris.\"\n"
        "   }\n"
        "   ```\n"
        "3. **If you need more information or cannot answer yet, select an appropriate tool and provide a clear, concise query.**\n"
        "4. **Think step-by-step.** Reflect on previous tool outputs and the question.\n"
        "5. **Do NOT repeat actions or search queries unless the previous attempt yielded an error.**\n\n"
        "**Retrieved Context:**\n{rag_context}\n\n"
        "**Current Context (Tool Outputs/Intermediate Info):**\n{context}\n\n"
        "**Previous Reasoning Steps:**\n{reasoning}\n\n"
        "**Current Task:** {current_task}\n"
        "**Current Thoughts:** {current_thoughts}\n\n"
        "**Question:** {question}\n\n"
        "**Expected JSON Output Format:**\n"
        "```json\n"
        "{\n"
        "  \"Reasoning\": \"Your reasoning process to decide the next step, including why a tool is chosen or how an answer is derived.\",\n"
        "  \"Action\": \"The name of the tool to use (e.g., duckduckgo_search, final answer, No Action), if no tool is needed yet, use 'No Action'.\",\n"
        "  \"Action Input\": \"The input for the tool (e.g., 'What is the capital of France?', 'The final answer is Paris.').\"\n"
        "}\n"
        "```\n"
        "Ensure your response is ONLY valid JSON and strictly follows this format. Begin your response with ````json`."
    )

    prompt = ChatPromptTemplate.from_messages([
        # --- CHANGE THIS LINE ---
        # Pass the system message as a tuple (role, content_template_string)
        ("system", system_prompt_template_str), 
        *state["history"] 
    ])

    # Format messages safely
    formatted_messages = prompt.format_messages(
        rag_context=rag_context,
        context=state.get("context", {}),
        reasoning=state.get("reasoning", ""),
        question=state.get("question", ""),
        current_task=state.get("current_task", ""),
        current_thoughts=state.get("current_thoughts", "")
    )
    
    # Format full input string
    try:
        full_input_string = tokenizer.apply_chat_template(
            formatted_messages, 
            tokenize=False, 
            add_generation_prompt=True
        )
    except Exception as e:
        print(f"WARNING: Failed to apply chat template: {e}. Using simple join.")
        full_input_string = "\n".join([msg.content for msg in formatted_messages])

    # Call LLM with retry
    def call_with_retry_local(inputs: str, retries: int = 3) -> AIMessage:
        for attempt in range(retries):
            try:
                response_text = llm.invoke(inputs) 
                
                # Strip the prompt from the generated text
                if response_text.startswith(inputs):
                    content = response_text[len(inputs):].strip()
                else:
                    content = response_text.strip() 

                print(f"DEBUG: RAW LOCAL LLM Response (Attempt {attempt+1}):\n---\n{content}\n---")
                
                # Attempt to parse to validate structure
                json.loads(content) 
                return AIMessage(content=content)
            except json.JSONDecodeError as e:
                print(f"[Retry {attempt+1}/{retries}] Invalid JSON. Error: {e}.")
                print(f"Invalid content: {content[:200]}...")
                state["history"].append(AIMessage(content=f"[Parsing Error] The previous LLM output was not valid JSON. Expected format: ```json{{\"Reasoning\": \"...\", \"Action\": \"...\", \"Action Input\": \"...\"}}```. Please ensure your response is ONLY valid JSON and strictly follows the format. Error: {e}"))
                time.sleep(3) 
            except Exception as e: 
                print(f"[Retry {attempt+1}/{retries}] Error: {e}.")
                state["history"].append(AIMessage(content=f"[LLM Error] Failed to get response: {e}. Trying again."))
                time.sleep(5) 
        return AIMessage(content='{"Reasoning": "Max retries exceeded", "Action": "Final Answer", "Action Input": "Error: Failed after multiple retries"}')

    response = call_with_retry_local(full_input_string) 
    content = response.content

    # Parse response
    reasoning, action, action_input = parse_agent_response(content)
    
    print(f"DEBUG: Parsed Action: '{action}', Action Input: '{action_input[:100]}...'")

    # Update state
    state["history"].append(AIMessage(content=content)) 
    state["reasoning"] += f"\nStep {state['iterations'] + 1}: {reasoning}"
    state["iterations"] += 1
    state["current_thoughts"] = reasoning 

    if "final answer" in action.lower():
        state["final_answer"] = action_input 
    else:
        state["context"]["pending_action"] = {
            "tool": action,
            "input": action_input
        }
        state["history"].append(AIMessage(content=f"Agent decided to use tool: {action} with input: {action_input}"))

    print(f"DEBUG: Exiting reasoning_node. New history length: {len(state['history'])}")
    return state

# ====== TOOL NODE ======
def tool_node(state: AgentState) -> AgentState:
    """Node for executing the chosen tool"""
    print(f"DEBUG: Entering tool_node. Iteration: {state.get('iterations', 0)}")
    
    # Safely get pending action
    tool_call_dict = state.get("context", {}).pop("pending_action", None)

    if not tool_call_dict:
        error_message = "[Tool Error] No pending_action found in context."
        print(f"ERROR: {error_message}")
        state.setdefault("history", []).append(AIMessage(content=error_message))
        return state

    tool_name = tool_call_dict.get("tool", "")
    tool_input = tool_call_dict.get("input", "")

    if not tool_name or not tool_input: 
        error_message = f"[Tool Error] Invalid action: Tool name '{tool_name}' or input '{tool_input}' was empty."
        print(f"ERROR: {error_message}") 
        state["history"].append(AIMessage(content=error_message))
        return state

    # Find and execute tool
    available_tools = state.get("tools", [])
    tool_fn = next((t for t in available_tools if t.name == tool_name), None)

    if tool_fn is None:
        tool_output = f"[Tool Error] Tool '{tool_name}' not found. Available: {', '.join([t.name for t in available_tools])}"
        print(f"ERROR: {tool_output}")
    else:
        try:
            print(f"DEBUG: Invoking tool '{tool_name}' with input: '{tool_input[:100]}...'")
            tool_output = tool_fn.run(tool_input)
            if tool_output is None:
                tool_output = f"[{tool_name} output] No result returned for '{tool_input}'."
        except Exception as e:
            tool_output = f"[Tool Error] Error running '{tool_name}': {str(e)}"
            print(f"ERROR: {tool_output}")

    state["history"].append(AIMessage(content=f"[{tool_name} output]\n{tool_output}"))
    
    print(f"DEBUG: Exiting tool_node. Tool output added to history.")
    return state

# ====== AGENT GRAPH ======
class StateGraph:
    """Simple state graph implementation"""
    def __init__(self, state: AgentState):
        self.nodes = {}
        self.entry_point = None
        self.edges = {}
        self.conditional_edges = {}
        
    def add_node(self, name: str, func: callable):
        self.nodes[name] = func
        
    def set_entry_point(self, name: str):
        self.entry_point = name
        
    def add_conditional_edges(self, source: str, condition: callable, path_map: Dict[str, str]):
        self.conditional_edges[source] = (condition, path_map)
        
    def add_edge(self, source: str, dest: str):
        self.edges[source] = dest
        
    def compile(self):
        def app(state: AgentState) -> AgentState:
            current_node = self.entry_point
            
            while current_node != END:
                if current_node in self.nodes:
                    state = self.nodes[current_node](state)
                    
                if current_node in self.conditional_edges:
                    condition, path_map = self.conditional_edges[current_node]
                    next_node_key = condition(state)
                    current_node = path_map.get(next_node_key, END)
                elif current_node in self.edges:
                    current_node = self.edges[current_node]
                else:
                    current_node = END
                    
            return state
            
        return app

END = "__END__"

# ====== AGENT INTERFACE ======
class BasicAgent:
    def __init__(self):
        # Instantiate tools (implementation not shown - should be defined elsewhere)
        self.tools = [
            duckduckgo_search, 
            wikipedia_search,
            arxiv_search,
            document_qa,
            python_execution,
            VideoTranscriptionTool()
        ]
        
        # Pre-initialize RAG vector store
        try:
            self.vector_store = create_vector_store()
            if not self.vector_store:
                print("WARNING: Vector store creation failed. Proceeding without RAG.")
        except Exception as e:
            print(f"ERROR creating vector store: {str(e)}")
            self.vector_store = None
            
        self.workflow = create_agent_workflow(self.tools)
    
    def __call__(self, question: str) -> str:
        print(f"\n--- Agent received question: {question[:80]}{'...' if len(question) > 80 else ''} ---")

        state = {
            "question": question,
            "context": {
                "vector_store": self.vector_store
            },
            "reasoning": "",
            "iterations": 0,
            "history": [HumanMessage(content=question)],
            "final_answer": None,
            "current_task": "Understand the question and plan the next step.",
            "current_thoughts": "",
            "tools": self.tools 
        }

        try:
            final_state = self.workflow.invoke(state)
            if final_state.get("final_answer") is not None:
                answer = final_state["final_answer"]
                print(f"--- Agent returning FINAL ANSWER: {answer} ---")
                return answer
            else:
                print("--- ERROR: Agent finished without setting 'final_answer' ---")
                if final_state.get("history"):
                    last_message = final_state["history"][-1].content
                    print(f"Last message: {last_message}")
                    return f"Agent could not answer. Last message: {last_message}"
                return "Error: Agent failed to provide an answer"
        except Exception as e:
            print(f"FATAL ERROR during agent execution: {str(e)}")
            return f"Agent encountered a fatal error: {str(e)}"

def create_agent_workflow(tools: List[Any]):
    workflow = StateGraph(AgentState)
    
    workflow.add_node("reason", reasoning_node)
    workflow.add_node("action", tool_node)
    
    workflow.set_entry_point("reason")
    
    workflow.add_conditional_edges(
        "reason",
        should_continue,
        {
            "action": "action",
            "reason": "reason",
            "end": END
        }
    )
    
    workflow.add_edge("action", "reason")
    
    app = workflow.compile()
    return app




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:
        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 = 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)