import os
from typing import TypedDict, List, Dict, Any, Optional
from langchain.agents import create_tool_calling_agent, AgentExecutor, initialize_agent
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.tools import tool
from langchain_core.messages import HumanMessage
from langchain_core.prompts import ChatPromptTemplate

# 1. Web Browsing
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.document_loaders import ImageCaptionLoader
import requests, time
import pandas as pd
from pypdf import PdfReader
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from youtube_transcript_api import YouTubeTranscriptApi

@tool
def web_search(query: str) -> str:
    """Allows search through DuckDuckGo.
    Args:
        query: what you want to search
    """
    search = DuckDuckGoSearchRun()
    results = search.invoke(query)
    return "\n".join(results)

@tool
def visit_webpage(url: str) -> str:
    """Fetches raw HTML content of a web page.
    Args:
        url: the webpage url
    """
    try:
        response = requests.get(url, timeout=5)
        return response.text
    except Exception as e:
        return f"[ERROR fetching {url}]: {str(e)}"

@tool
def wiki_search(query: str) -> str:
    """Wiki search tools.
    Args:
        query: what you want to wiki
    """
    api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100)
    wikipediatool = WikipediaQueryRun(api_wrapper=api_wrapper)
    return wikipediatool.run({"query": query})


@tool
def youtube_transcript(video_url: str) -> str:
    """Fetched youtube transcript
    Args:
        video_url: YouTube video url
    """
    try:
        video_id = video_url.split("v=")[-1].split("&")[0]
        transcript = YouTubeTranscriptApi.get_transcript(video_id)
        return " ".join([item["text"] for item in transcript])
    except Exception as e:
        return f"Error fetching transcript: {str(e)}"

# 4. File Reading
@tool
def read_file(dir: str) -> str:
    """Read the content of the provided file
    Args:
        dir: the filepath
    """
    extension = dir.split['.'][-1]
    if extension == 'xlsx':
        dataframe = pd.read_excel(dir)
        return dataframe.to_string()
    elif extension == 'pdf':
        reader = PdfReader(dir)
        contents = [p.extract_text() for p in reader.pages]
        return "\n".join(contents)
    else:
        with open(dir) as f:
            return f.read()

# 5. Image Open
@tool
def image_caption(dir: str) -> str:
    """Understand the content of the provided image
    Args:
        dir: the image url link
    """
    loader = ImageCaptionLoader(images=[dir])
    metadata = loader.load()
    return metadata[0].page_content

# 2. Coding
# 3. Multi-Modality

# ("human", f"Question: {question}\nReport to validate: {final_answer}")
class BasicAgent:
    def __init__(self):
        self.model = ChatGoogleGenerativeAI(
            model="gemini-2.0-flash",
            temperature=0,
            max_tokens=128,
            timeout=None,
            max_retries=2,
            google_api_key="AIzaSyAxVUPaGJIgdxB46ZR0RWPKSjB9a63Z80o",
            # other params...
        )
        # System Prompt for few shot prompting
        self.sys_prompt = """"
                You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
                YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separared list of numbers and/or strings.
                If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise.
                If you are asked for a string, don't use articles, neither abbreviations (eg. for cities), and write the digits in plain text unless specified otherwise.
                If you are asked for a comma separated list, apply the above rules depending of whether the element to put in the list is a number or a string.

                You have access to the following tools:
                - web_search: web search the content of the query by passing the query as input
                - visit_webpage: visit the given webpage url by passing the url as input
                - wiki_search: wiki search the content of the query by passing the query as input if the question asks for wiki search it
                - youtube_transcript: fetch the transcript of the Youtube video by passing the video url as input if the question asks for watching a Youtube video
                - read_file: read the content of the attached file by passing the file directory as input
                - image_caption: understand the visual content of the attached image by passing the image directory as input

                HERE are some examples illustrating how and what tools to call.
                ---------------
                TASK: Count how many birds in the provided Youtube video.
                ACTION: Call youtube_transcript tool to extract video transcript. Use LLM to understand the retrived transcript.

                TASK: How many Grammy Awards that Taylor Swift has won.
                ACTION: Call the web_search tools with the query: 'how many Grammy Awards that Taylor Swift has won.' to extract the answer.

                TASK: Count how many people in this image.
                ACTION: Call the image_caption tool by passing the image directory as input. Then, use LLM to understand the image caption and answer the question.

                TASK: How much the total expense in this spreadsheet?
                ACTION: Call the read_file tool to extract the content of the provided spreadfile. Then, use LLM to extract the amount of every expense and sum them up.

                TASK: How many All England Title that Lee Chong Wei won?
                ACTION: Call wiki_search with the query: "Lee Chong Wei". Extract the relevant row of All England Title and count how many rows is there.
        """
        self.tools = [web_search, visit_webpage, wiki_search, youtube_transcript, read_file, image_caption]
        self.prompt = ChatPromptTemplate.from_messages([
            ("system", self.sys_prompt),
            ("human", "{input}")
        ])
        self.agent = initialize_agent(
            tools=self.tools,
            llm=self.model,
            agent="zero-shot-react-description",  # ReAct agent type
            verbose=True,
            system_prompt=self.prompt
        )
        print("BasicAgent initialized.")
    
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        # response = self.agent_exe.invoke({"input": f"Question: {question}"})
        # fixed_answer = response['message'][-1].content
        time.sleep(15)
        fixed_answer = self.agent.run(f"Answer this question: {question}")
        # fixed_answer = "This is a default answer."
        print(f"Agent returning fixed answer: {fixed_answer}")
        return fixed_answer