import os
import time
import gradio as gr
import asyncio
import requests
import inspect
import pandas as pd
import re

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

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

from typing import TypedDict, List, Dict, Any, Optional, Annotated
from langgraph.graph import StateGraph, START, END
# from langchain_openai import ChatOpenAI
# from langchain_huggingface.llms import HuggingFaceEndpoint
from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage
from langgraph.graph.message import add_messages
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_community.tools import WikipediaQueryRun
from langchain_community.document_loaders import YoutubeLoader, WebBaseLoader
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
from langgraph.prebuilt import ToolNode, tools_condition
from langchain.tools import tool

# Initialize the Hugging Face model

llm = HuggingFaceEndpoint(
    repo_id="Qwen/Qwen3-30B-A3B", # "Qwen/Qwen2.5-72B-Instruct",
    provider="nebius", # "hf-inference",
    max_new_tokens=8192,
    do_sample=False,
    # temperature=0.,
)

chat_model = ChatHuggingFace(llm=llm)

# Define tools


@tool
def fetch_website(url:str) -> str:
    """Fetch the content of a website.
    Args:
        url: The URL of the website to fetch.
    Returns:
        The title and content of the website.
    """
    loader = WebBaseLoader(url)
    docs = loader.load()
    return docs[0].page_content

@tool
def ask_wiki(query: str) -> str:
    """Retrieve information from Wikipedia based on a user query.
    Args:
        query: A user query.
    Returns:
        A single string containing the retrieved article from Wikipedia.
    """
    if not query.strip():
        return "Please provide a valid query."
    try:
        wiki_toolapi_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=8000)
        wiki_tool = WikipediaQueryRun(api_wrapper=wiki_toolapi_wrapper)
        result = wiki_tool.run(query)
        return result
    except Exception as e:
        return f"Error retrieving information: {str(e)}"

@tool
def youtube_transcript(url: str) -> str:
    """Retrieve transcript from Youtube based url.
    Args:
        url: input youtube url.
    Returns:
        A single string containing the transcript of the youtube videos.
    """
    max_attempts = 5  # Set a maximum number of attempts
    attempts = 0
    loader = YoutubeLoader.from_youtube_url(url, add_video_info=True)
    while attempts < max_attempts:
        try:
            docs  = loader.load()
            return docs[0].page_content
        except Exception as e:
            attempts += 1
            print(f"Attempt {attempts} failed: {e}")
            # Optionally add a delay before retrying
            time.sleep(1) # Import the time module
    return "Failed to retrieve transcript after multiple attempts."

# Equip llm with tools
tools_list = [
    fetch_website,
    ask_wiki,
    youtube_transcript,
]

llm_with_tools = chat_model.bind_tools(
    tools_list, 
    # parallel_tool_calls=False
)

# Define Agent Workflow

class AgentState(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]


def assistant(state: AgentState):
    # System message
    textual_description_of_tool="""
fetch_website(url: str) -> str:
    Fetch the content of a website.
    Args:
        url: The URL of the website to fetch.
    Returns:
        The title and content of the website.

ask_wiki(query: str) -> str:
    Retreive information from Wikipedia based on a user query.
    Args:
        query: A user query.
    Returns:
        A single string containing the retrieved article from Wikipedia.

youtube_transcript(url: str) -> str:
    Fetch the transcript of a youtube video.
    Args:
        url: input youtube url.
    Returns:
        A single string containing the transcript of the youtube videos.
"""
    sys_msg = SystemMessage(
        content=f"You are a helpful assistant at answering user questions. Your final answer will be between <answer> and </answer> tags. You can access provided tools:\n{textual_description_of_tool}\n"
    )

    return {
        "messages": [llm_with_tools.invoke([sys_msg] + state["messages"])],
    }

# Build the StateGraph for the agent
# The graph
builder = StateGraph(AgentState)

# Define nodes: these do the work
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools_list))

# Define edges: these determine how the control flow moves
builder.add_edge(START, "assistant")
builder.add_conditional_edges(
    "assistant",
    # If the latest message requires a tool, route to tools
    # Otherwise, provide a direct response
    tools_condition,
)
builder.add_edge("tools", "assistant")
agent_graph = builder.compile()

def extract_answer(text):
    match = re.search(r'<answer>(.*?)</answer>', text, re.DOTALL)
    if match:
        return match.group(1).strip()
    return 'None'

class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
    async def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        # fixed_answer = "This is a default answer."
        # print(f"Agent returning fixed answer: {fixed_answer}")
        # Create agent with all the tools
        
        # Example query agent might receive
        # fixed_answer = await agent.run(question)
        messages = [
            HumanMessage(
                # content="Who is Barack Obama?"
                # content="Divide 6790 by 5"
                content=question # + '/nothink'
            )
        ]
        response = await agent_graph.ainvoke({"messages": messages}, config={"recursion_limit": 10})
        response_text = response['messages'][-1].content
        # return response_text.split('</think>')[-1]
        return extract_answer(response_text)


async 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 = await 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)