# -*- coding: utf-8 -*-

import os
import re
import time
import json
import cv2
import requests
import hashlib
import inspect
import functools
from math import sqrt
from time import sleep
from collections import Counter
from typing import Optional, List, Dict, Callable

import pandas as pd
import gradio as gr
import dateparser
import dataclasses

from langchain_core.language_models import LLM
from langchain_core.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_core.documents import Document

from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader

from smolagents import CodeAgent, tool, InferenceClientModel


class GeminiLLM(LLM):
    """Wrapper para usar Google Gemini como un LLM de LangChain."""

    api_key: str = os.getenv("GEMINI")
    fallback_api_key: str = os.getenv("GEMINI2")
    model_name: str = "gemini-2.0-flash"
    temperature: float = 0.1

    @property
    def _llm_type(self) -> str:
        return "google-gemini-llm"

    def _make_request(self, api_key: str, prompt: str) -> requests.Response:
        url = f"https://generativelanguage.googleapis.com/v1beta/models/{self.model_name}:generateContent"
        headers = {
            "Content-Type": "application/json",
            "X-goog-api-key": api_key
        }

        full_prompt = (
            "You are a helpful agent that answers questions concisely and accurate and strictly follows instructions.\n"
            "Respond ONLY with the requested information, no explanations or extra words. If the question specifies a format (number, name, comma separated list), follow it exactly.\n"
            f"Question: {prompt}"
        )

        data = {
            "contents": [
                {
                    "role": "user",
                    "parts": [
                        {"text": full_prompt}
                    ]
                }
            ],
            "generationConfig": {
                "temperature": self.temperature
            }
        }

        return requests.post(url, headers=headers, json=data)



    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        """Envía el prompt a la API de Gemini y devuelve la respuesta.
           Si la cuota se supera, intenta con la API key alternativa."""

        if not self.api_key:
            raise ValueError("Debes proporcionar una API Key válida de Gemini.")

        response = self._make_request(self.api_key, prompt)

        # Si el error es por cuota y hay fallback API key definida, intentar con la fallback
        if response.status_code == 403 and "quota" in response.text.lower():
            if self.fallback_api_key:
                time.sleep(3)  # Simula latencia opcional

                response = self._make_request(self.fallback_api_key, prompt)
            else:
                return f"Error {response.status_code}: {response.text} (no hay API key alternativa)"

        if response.status_code == 200:
            result = response.json()
            return result["candidates"][0]["content"]["parts"][0]["text"]
        else:
            return f"Error {response.status_code}: {response.text}"





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

@dataclasses.dataclass
class WikiSourceDocument:
    source: str
    page: str
    page_content: str

# --- Search Tools ---
@tool
def wiki_search(query: str, load_max_docs: int = 3) -> List[WikiSourceDocument]:
    """
    Search Wikipedia and return a list of documents.

    Args:
        query (str): The search query to look up on Wikipedia.
        load_max_docs (int): The maximum number of documents to retrieve.

    Returns:
        List[WikiSourceDocument]: A list of documents containing source, page, and content.
    """
    search_docs = WikipediaLoader(query=query, load_max_docs=load_max_docs).load()
    return search_docs

@tool
def web_search(query: str, max_results: int = 3) -> Dict[str, str]:
    """
    Perform a web search and return summarized results.

    Args:
        query (str): The search query to look up on the web.
        max_results (int): The maximum number of search results to retrieve.

    Returns:
        Dict[str, str]: A dictionary containing the web search results.
    """
    search_docs = TavilySearchResults(max_results=max_results).invoke(input=query)
    return {"web_results": search_docs}

@tool
def arxiv_search(query: str, load_max_docs: int = 3) -> Dict[str, str]:
    """
    Search Arxiv and return formatted research documents.

    Args:
        query (str): The search query for scientific papers.
        load_max_docs (int): The maximum number of documents to retrieve.

    Returns:
        Dict[str, str]: A dictionary containing formatted Arxiv search results.
    """
    search_docs = ArxivLoader(query=query, load_max_docs=load_max_docs).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document Title="{doc.metadata["Title"]}" Published="{doc.metadata["Published"]}" '
            f'Authors="{doc.metadata["Authors"]}" Summary="{doc.metadata["Summary"]}"/>\n'
            f'{doc.page_content}\n</Document>'
            for doc in search_docs
        ]
    )
    return {"arxiv_results": formatted_search_docs}

def extract_keywords(text: str) -> list:
    """
    Simple keyword extractor that splits text into unique keywords.
    
    Args:
        text (str): Input text.

    Returns:
        list: List of extracted keywords.
    """
    words = text.lower().split()
    keywords = list(set([w.strip(".,!?") for w in words if len(w) > 3]))
    return keywords

import re

def calculate_expression(expression: str) -> str:
    """
    Evaluates a simple mathematical expression and returns the result.

    Args:
        expression (str): A math expression (e.g., "12 * (3+5) / 4").

    Returns:
        str: The result of the calculation or an error message if invalid.
    """
    try:
        # Allow only numbers, operators, parentheses, decimal points, and spaces
        if not re.match(r'^[\d\s\+\-\*\/\(\)\.]+$', expression):
            return "Invalid characters detected in expression."
        
        result = eval(expression)
        return str(result)
    except Exception as e:
        return f"Error evaluating expression: {str(e)}"

def basic_calculator(a: float, b: float, operation: str) -> str:
    """
    Perform basic arithmetic operations between two numbers.

    Args:
        a (float): First number.
        b (float): Second number.
        operation (str): The operation to perform. Options: "add", "subtract", "multiply", "divide".

    Returns:
        str: The result of the calculation or an error message.
    """
    try:
        if operation == "add":
            return str(a + b)
        elif operation == "subtract":
            return str(a - b)
        elif operation == "multiply":
            return str(a * b)
        elif operation == "divide":
            if b == 0:
                return "Error: Division by zero is not allowed."
            return str(a / b)
        else:
            return "Invalid operation. Use add, subtract, multiply, or divide."
    except Exception as e:
        return f"Error: {str(e)}"

def sort_list(items: list, reverse: bool = False):
    """
    Sort a list of numbers or strings in ascending or descending order.
    Returns a stringified list to avoid NoneType errors in the agent.
    """
    try:
        if not isinstance(items, list):
            return "Error: Input must be a list."
        return str(sorted(items, reverse=reverse))
    except Exception as e:
        return f"Error sorting list: {str(e)}"



# --- Agente básico optimizado para preguntas ---
class BasicAgent:
    def __init__(self, llm=None, max_iterations=3):
        self.llm = llm or GeminiLLM()
        # Sólo herramientas de búsqueda y extracción textual clave
        self.tools = {
            "wiki_search": wiki_search,
            "web_search": web_search,
            "arxiv_search": arxiv_search,
            "extract_keywords": extract_keywords,
            "calculate_expression":calculate_expression,
            "basic_calculator":basic_calculator,
            "sort_list":sort_list

          
        }
        self._cache = {}
        self.max_iterations = max_iterations

        # Descripción simplificada de herramientas para el prompt
        tools_desc = "\n".join(
            f"- {name}: {(func.__doc__ or 'No description available').strip().splitlines()[0]}"
            for name, func in self.tools.items()
        )

        self.prompt_template = PromptTemplate.from_template(tools_desc)
        self.chain = LLMChain(prompt=self.prompt_template, llm=self.llm)

    def _cache_key(self, tool_name, args, kwargs):
        key_data = {"tool": tool_name, "args": args, "kwargs": kwargs}
        key_json = json.dumps(key_data, sort_keys=True, default=str)
        return hashlib.md5(key_json.encode()).hexdigest()

    def call_tool(self, tool_name: str, *args, **kwargs):
        func = self.tools.get(tool_name)
        if not func:
            return f"Tool '{tool_name}' not found."

        key = self._cache_key(tool_name, args, kwargs)
        if key in self._cache:
            return self._cache[key]

        try:
            result = func(*args, **kwargs)
            self._cache[key] = result
            return result
        except Exception as e:
            return f"Error executing tool '{tool_name}': {e}"

    def _parse_arg(self, arg: str):
        arg = arg.strip()
        if arg.lower() in ("true", "false"):
            return arg.lower() == "true"
        try:
            return int(arg)
        except:
            pass
        try:
            return float(arg)
        except:
            pass
        if (arg.startswith('"') and arg.endswith('"')) or (arg.startswith("'") and arg.endswith("'")):
            return arg[1:-1]
        try:
            return json.loads(arg)
        except:
            pass
        return arg

    def _run_once(self, text: str) -> (str, bool):
        llm_out = self.chain.run({"question": text})
        pattern = r"tool:(\w+)\((.*?)\)"
        tools_called = False

        def repl(m):
            nonlocal tools_called
            tools_called = True
            tool_name = m.group(1)
            args_raw = m.group(2)
            args = [self._parse_arg(a) for a in re.findall(r'(?:[^,"]|"(?:\\.|[^"])*")+', args_raw)] if args_raw.strip() else []
            res = self.call_tool(tool_name, *args)
            return str(res)

        processed = re.sub(pattern, repl, llm_out)
        return processed, tools_called

    def __call__(self, question: str) -> str:
        text = question
        for _ in range(self.max_iterations):
            text, used_tools = self._run_once(text)
            if not used_tools:
                break
        return text







# --- Build Gradio Interface using Blocks ---
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


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)