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()


from langgraph.graph import END, StateGraph
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
from langchain_core.tools import tool
from typing import Dict, List, TypedDict, Annotated
import operator
from langchain_community.llms import HuggingFaceHub
from langchain_community.chat_models import ChatHuggingFace


from langchain.schema import HumanMessage  # Or your framework's equivalent

def init_state(question: str):
    return {
        "question": question,
        "history": [HumanMessage(content=question)],
        "context": {}  # <- Add this line
    }



# ====== Tool Definitions ======
@tool
def duckduckgo_search(query: str) -> str:
    """Search web using DuckDuckGo. Returns top 3 results."""
    from duckduckgo_search import DDGS
    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)
        )

@tool
def wikipedia_search(query: str) -> str:
    """Get Wikipedia summaries. Returns first 3 sentences."""
    import wikipedia
    try:
        return wikipedia.summary(query, sentences=3)
    except wikipedia.DisambiguationError as e:
        return f"Disambiguation options: {', '.join(e.options[:3])}"
    except wikipedia.PageError:
        return "Page not found"

@tool
def arxiv_search(query: str) -> str:
    """Search academic papers on arXiv. Returns top 3 results."""
    import arxiv
    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
    )

@tool
def document_qa(input_str: str) -> str:
    """Answer questions from documents. Input format: 'document_text||question'"""
    from transformers import pipeline
    if '||' not in input_str:
        return "Invalid format. Use: 'document_text||question'"
    
    context, question = input_str.split('||', 1)
    qa_model = pipeline('question-answering', model='deepset/roberta-base-squad2')
    return qa_model(question=question, context=context)['answer']

@tool
def python_execution(code: str) -> str:
    """Execute Python code and return output."""
    try:
        # Create isolated environment
        env = {}
        exec(f"def __exec_fn__():\n    {indent_code(code)}\nresult = __exec_fn__()", env)
        return str(env.get('result', 'No output'))
    except Exception as e:
        return f"Error: {str(e)}"

from typing import Optional
from langchain_core.tools import BaseTool
from youtube_transcript_api import YouTubeTranscriptApi

class VideoTranscriptionTool(BaseTool):
    name: str = "transcript_video"
    description: str = "Fetch text transcript from YouTube videos using URL or ID. Optionally include timestamps."

    def _run(self, url: str, include_timestamps: Optional[bool] = False) -> str:
        # Extract video ID
        video_id = None
        if "youtube.com/watch?v=" in url:
            video_id = url.split("v=")[1].split("&")[0]
        elif "youtu.be/" in url:
            video_id = url.split("youtu.be/")[1].split("?")[0]
        elif len(url.strip()) == 11 and not ("http://" in url or "https://" in url):
            video_id = url.strip()

        if not video_id:
            return f"Invalid or unsupported YouTube URL/ID: {url}"

        try:
            transcription = YouTubeTranscriptApi.get_transcript(video_id)

            if include_timestamps:
                formatted = []
                for part in transcription:
                    timestamp = f"{int(part['start']//60)}:{int(part['start']%60):02d}"
                    formatted.append(f"[{timestamp}] {part['text']}")
                return "\n".join(formatted)
            else:
                return " ".join([part['text'] for part in transcription])

        except Exception as e:
            return f"Error fetching transcript: {str(e)}"

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





import os
import time
import json
from typing import TypedDict, List, Union, Any, Dict
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.schema import HumanMessage, AIMessage, SystemMessage
from langchain.prompts import ChatPromptTemplate
from langgraph.graph import StateGraph, END
from google.api_core.exceptions import ResourceExhausted

# Assume these tools are defined elsewhere and imported
# Placeholder for your actual tool implementations
# For example:
# from your_tools_module import duckduckgo_search, wikipedia_search, arxiv_search, document_qa, python_execution
# And ensure you have a proper VideoTranscriptionTool
def duckduckgo_search(query: str) -> str:
    """Performs a DuckDuckGo search for current events or general facts."""
    # Placeholder for actual implementation
    print(f"DEBUG: duckduckgo_search called with: {query}")
    return f"Search result for '{query}': Example relevant information from web."

def wikipedia_search(query: str) -> str:
    """Searches Wikipedia for encyclopedic information."""
    # Placeholder for actual implementation
    print(f"DEBUG: wikipedia_search called with: {query}")
    return f"Wikipedia result for '{query}': Found detailed article."

def arxiv_search(query: str) -> str:
    """Searches ArXiv for scientific preprints and papers."""
    # Placeholder for actual implementation
    print(f"DEBUG: arxiv_search called with: {query}")
    return f"ArXiv result for '{query}': Found relevant research paper."

def document_qa(document_path: str, question: str) -> str:
    """Answers questions based on the content of a given document file (PDF, DOCX, TXT)."""
    # Placeholder for actual implementation
    print(f"DEBUG: document_qa called with: {document_path}, question: {question}")
    return f"Document QA result for '{question}': Answer extracted from document."

def python_execution(code: str) -> str:
    """Executes Python code in a sandboxed environment for calculations or data manipulation."""
    # Placeholder for actual implementation - IMPORTANT: Implement this securely!
    # Example (UNSAFE for real use without proper sandboxing):
    try:
        exec_globals = {}
        exec_locals = {}
        exec(code, exec_globals, exec_locals)
        return str(exec_locals.get('result', 'Code executed, no explicit result assigned to "result" variable.'))
    except Exception as e:
        return f"Python execution error: {str(e)}"

class VideoTranscriptionTool:
    """Transcribes and analyzes video content from a URL or ID."""
    def __call__(self, video_id_or_url: str) -> str:
        # Placeholder for actual implementation using youtube-transcript-api etc.
        print(f"DEBUG: VideoTranscriptionTool called with: {video_id_or_url}")
        return f"Video transcription/analysis result for '{video_id_or_url}': Summary of video content."


# --- Agent State Definition ---
class AgentState(TypedDict):
    question: str
    history: List[Union[HumanMessage, AIMessage, Dict[str, Any]]] # Allows for tool calls as dicts
    context: Dict[str, Any]
    reasoning: str
    iterations: int
    final_answer: Union[str, float, int, None]
    current_task: str # Added for more focused reasoning
    current_thoughts: str # Added for more focused reasoning

# --- Utility Functions ---
def parse_agent_response(response_content: str) -> tuple[str, str, str]:
    """
    Parses the LLM's JSON output for reasoning, action, and action input.
    """
    try:
        response_json = json.loads(response_content)
        reasoning = response_json.get("Reasoning", "").strip()
        action = response_json.get("Action", "").strip()
        action_input = response_json.get("Action Input", "").strip()
        return reasoning, action, action_input
    except json.JSONDecodeError:
        # Fallback for when LLM doesn't return perfect JSON (less likely with good prompt)
        print(f"WARNING: LLM response not perfectly JSON: {response_content[:200]}...")
        # Attempt heuristic parsing as a last resort
        reasoning_match = response_content.split("Reasoning:", 1)
        reasoning = reasoning_match[1].split("Action:", 1)[0].strip() if len(reasoning_match) > 1 else ""

        action_part_match = response_content.split("Action:", 1)
        action_part = action_part_match[1].strip() if len(action_part_match) > 1 else ""

        action_input_match = action_part.split("Action Input:", 1)
        action = action_input_match[0].strip()
        action_input = action_input_match[1].strip() if len(action_input_match) > 1 else ""
        return reasoning, action, action_input


# --- Graph Nodes ---

def should_continue(state: AgentState) -> str:
    """
    Determines if the agent should continue reasoning, use a tool, or end.
    """
    history = state.get("history", [])

    # Check for final answer in the last AIMessage
    if history and isinstance(history[-1], AIMessage) and "FINAL ANSWER:" in history[-1].content:
        print("DEBUG: should_continue -> END (Final Answer detected)")
        return "end"
    
    # Check if a tool was just executed (its output is in history)
    # and the next step should be reasoning over that output
    for msg in reversed(history):
        if isinstance(msg, AIMessage) and any(f"[{tool.name} output]" in msg.content for tool in state.get("tools", [])):
            print("DEBUG: should_continue -> REASON (Tool output detected, need to process)")
            return "reason"

    # Check if there's an action request to be executed
    # This happens *after* reasoning has determined a tool is needed,
    # but *before* the tool has run.
    for msg in reversed(history):
        if isinstance(msg, dict) and msg.get("type") == "action_request":
            print("DEBUG: should_continue -> ACTION (Action request pending)")
            return "action"

    # If nothing else, assume we need to reason
    print("DEBUG: should_continue -> REASON (Default to reasoning)")
    return "reason"


def reasoning_node(state: AgentState) -> AgentState:
    """
    Node for the agent to analyze the question, determine next steps,
    and select tools.
    """
    print(f"DEBUG: Entering reasoning_node. Iteration: {state['iterations']}")
    print(f"DEBUG: Current history length: {len(state.get('history', []))}")

    # Load API key
    GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
    if not GOOGLE_API_KEY:
        raise ValueError("GOOGLE_API_KEY not set in environment variables.")

    # Ensure history is well-formed for the LLM prompt
    if "history" not in state or not isinstance(state["history"], list):
        state["history"] = []
    
    # Initialize/update state fields
    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", "")

    # Create Gemini model wrapper
    llm = ChatGoogleGenerativeAI(
        model="gemini-1.5-flash", # Use a fast model for agentic loops
        temperature=0.1, # Keep it low for more deterministic reasoning
        google_api_key=GOOGLE_API_KEY
    )

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

    # Craft a more robust and explicit system prompt
    system_prompt = (
        "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.\n"
        "- Use **wikipedia_search** for encyclopedic information, historical context, or detailed topics.\n"
        "- Use **arxiv_search** for scientific papers, research, or cutting-edge technical information.\n"
        "- Use **document_qa** when the question explicitly refers to a specific document file (e.g., 'Analyze this PDF').\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.\n"
        "- Use **VideoTranscriptionTool** for any question involving video or audio content.\n\n"
        "**Current Context:**\n{context}\n\n"
        "**Previous Reasoning Steps:**\n{reasoning}\n\n"
        "**Current Task:** {current_task}\n"
        "**Current Thoughts:** {current_thoughts}\n\n"
        "**Your Response MUST be a valid JSON object with the following keys:**\n"
        "```json\n"
        "{\n"
        "  \"Reasoning\": \"Your detailed analysis of the question and why you chose a specific action.\",\n"
        "  \"Action\": \"[Tool name OR 'Final Answer']\",\n"
        "  \"Action Input\": \"[Input for the selected tool OR the final response]\"\n"
        "}\n"
        "```\n"
        "Ensure 'Action Input' is appropriate for the chosen 'Action'. If 'Action' is 'Final Answer', provide the complete, concise answer."
    )

    prompt = ChatPromptTemplate.from_messages([
        SystemMessage(content=system_prompt),
        *state["history"] # Include full history for conversational context
    ])

    chain = prompt | llm

    # === Add Retry Logic ===
    def call_with_retry(inputs, retries=3, delay=60):
        for attempt in range(retries):
            try:
                response = chain.invoke(inputs)
                # Attempt to parse immediately to catch bad JSON before returning
                parse_agent_response(response.content) 
                return response
            except ResourceExhausted as e:
                print(f"[Retry {attempt+1}/{retries}] Gemini rate limit hit. Waiting {delay}s...")
                time.sleep(delay)
            except json.JSONDecodeError as e:
                print(f"[Retry {attempt+1}/{retries}] LLM returned invalid JSON. Retrying...")
                print(f"Invalid JSON content: {response.content[:200]}...")
                time.sleep(5) # Shorter delay for parsing errors
            except Exception as e:
                print(f"[Retry {attempt+1}/{retries}] An unexpected error occurred during LLM call: {e}. Retrying...")
                time.sleep(delay)
        raise RuntimeError("Failed after multiple retries due to Gemini quota limit or invalid JSON.")

    # Call model with retry protection
    response = call_with_retry({
        "context": state["context"],
        "reasoning": state["reasoning"],
        "question": state["question"], # Redundant as it's in history, but keeps prompt consistent
        "current_task": state["current_task"],
        "current_thoughts": state["current_thoughts"]
    })

    # Parse output using the robust JSON parser
    content = response.content
    reasoning, action, action_input = parse_agent_response(content)
    
    print(f"DEBUG: LLM Response Content: {content[:200]}...")
    print(f"DEBUG: Parsed Action: {action}, Action Input: {action_input[:100]}...")

    # Update state
    state["history"].append(AIMessage(content=content)) # Store the raw LLM response
    state["reasoning"] += f"\nStep {state['iterations'] + 1}: {reasoning}"
    state["iterations"] += 1
    state["current_thoughts"] = reasoning # Update current thoughts for next iteration

    if "final answer" in action.lower():
        state["history"].append(AIMessage(content=f"FINAL ANSWER: {action_input}"))
        state["final_answer"] = action_input # Set final answer directly in state
    else:
        # Store the action request in history for tool_node
        state["history"].append({
            "type": "action_request",
            "tool": action,
            "input": action_input
        })

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


def tool_node(state: AgentState) -> AgentState:
    # ... (previous code)

    tool_call_dict = None
    for msg in reversed(state["history"]):
        if isinstance(msg, dict) and msg.get("type") == "action_request":
            tool_call_dict = msg
            break

    if not tool_call_dict:
        print("WARNING: No action_request found in history, skipping tool execution.")
        return state # Or raise a more specific error if this truly shouldn't happen

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

    # --- ADD THIS DEBUG PRINT ---
    print(f"DEBUG: tool_node received action_request: tool='{tool_name}', input='{tool_input[:100]}...'")
    # --- END DEBUG PRINT ---

    if not tool_name or tool_input is None: # tool_input can be empty string for some tools, but not None
        print(f"ERROR: Invalid tool call in action_request. Tool name: '{tool_name}', Input: '{tool_input}'")
        # Instead of raising directly, you might want to send this back to reasoning
        # Or provide a specific error message as tool output
        state["history"].append(AIMessage(content=f"[Tool Error] Invalid tool call: Tool name '{tool_name}' or input was empty. LLM needs to provide valid action."))
        return state

    # Look up and invoke the tool from the state's tool list
    available_tools = state.get("tools", [])
    tool_fn = next((t for t in available_tools if t.name == tool_name), None) # Assuming tools are LangChain Tool objects now

    if tool_fn is None:
        # Fallback for unrecognized tool - feedback to LLM
        tool_output = f"[Tool Error] Tool '{tool_name}' not found or not available. Please choose from: {', '.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) # Assuming tool.run() method for LangChain Tools
            if not tool_output: # Handle empty tool output
                tool_output = f"[{tool_name} output] No specific result found for '{tool_input}'. The tool might have returned an empty response."
        except Exception as e:
            tool_output = f"[Tool Error] An error occurred while running '{tool_name}': {str(e)}"
            print(f"ERROR: {tool_output}")

    # Add output to history as an AIMessage
    # Ensure the history only contains HumanMessage and AIMessage objects for LangGraph's internal processing.
    # The action_request dict can be removed or transformed if it's no longer needed for internal state.
    # For now, we'll just add the 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. New history length: {len(state['history'])}")
    return state


# ====== Agent Graph ======
def create_agent_workflow(tools: List[Any]): # tools are passed in now
    workflow = StateGraph(AgentState)
    
    # Define nodes
    workflow.add_node("reason", reasoning_node)
    workflow.add_node("action", tool_node)
    
    # Set entry point
    workflow.set_entry_point("reason")
    
    # Define edges
    workflow.add_conditional_edges(
        "reason",
        should_continue,
        {
            "action": "action", # Go to action node if a tool is requested
            "reason": "reason", # Loop back to reason if more thinking is needed
            "end": END          # End if final answer detected
        }
    )
    
    workflow.add_edge("action", "reason") # Always go back to reasoning after a tool action
    
    # Compile the graph
    app = workflow.compile()
    
    # Pass tools into the state so nodes can access them.
    # This is a bit of a hacky way to get them into the state, but works for now.
    # A cleaner way might be to make `tool_node` receive tools as a closure or directly from agent init.
    # For this example, we'll modify the initial state for each invocation.
    return app


# ====== Agent Interface ======
class BasicAgent:
    def __init__(self):
        # Tools need to be LangChain Tool objects for name and description
        from langchain.tools import Tool
        self.tools = [
            Tool(name="duckduckgo_search", func=duckduckgo_search, description="Performs a DuckDuckGo search for current events or general facts."),
            Tool(name="wikipedia_search", func=wikipedia_search, description="Searches Wikipedia for encyclopedic information."),
            Tool(name="arxiv_search", func=arxiv_search, description="Searches ArXiv for scientific preprints and papers."),
            Tool(name="document_qa", func=document_qa, description="Answers questions based on the content of a given document file (PDF, DOCX, TXT). Requires 'attachment_path' and 'question' as input."),
            Tool(name="python_execution", func=python_execution, description="Executes Python code in a sandboxed environment for complex calculations or data manipulation."),
            Tool(name="VideoTranscriptionTool", func=VideoTranscriptionTool(), description="Transcribes and analyzes video content from a URL or ID. Use for any question involving video or audio.")
        ]
        self.workflow = create_agent_workflow(self.tools) # Pass tools to workflow creator

    def __call__(self, question: str) -> str:
        print(f"\n--- Agent received question: {question[:50]}{'...' if len(question) > 50 else ''} ---")

        # Initialize state with proper structure and pass tools
        state = {
            "question": question,
            "context": {},
            "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 # Pass tools into the state
        }

        # Invoke the workflow
        final_state = self.workflow.invoke(state)

        # Extract the FINAL ANSWER from history
        if final_state.get("final_answer"):
            answer = final_state["final_answer"]
            print(f"--- Agent returning FINAL ANSWER: {answer} ---")
            return answer
        
        # Fallback if final_answer wasn't set correctly in state
        for msg in reversed(final_state["history"]):
            if isinstance(msg, AIMessage) and "FINAL ANSWER:" in msg.content:
                answer = msg.content.split("FINAL ANSWER:")[1].strip()
                print(f"--- Agent returning FINAL ANSWER (from history): {answer} ---")
                return answer

        print(f"--- ERROR: No FINAL ANSWER found in agent history for question: {question} ---")
        raise ValueError("No FINAL ANSWER found in agent history.")

    


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)