import asyncio
import re
from pydantic_ai.result import RunResult
import streamlit as st
from pydantic_ai import Agent, RunContext, Tool
from pydantic_ai.models.groq import GroqModel
import nest_asyncio
from pydantic_ai.messages import ModelMessage
import pdfplumber
from transformers import pipeline
import torch
import os
import presentation as customClass
from streamlit_pdf_viewer import pdf_viewer
from dataclasses import dataclass

api_key = os.getenv("API_KEY")
data = []
last_message = ''
result_data: list[customClass.PPT] = []

# to generate ppt
model = GroqModel("llama-3.2-90b-vision-preview", api_key=api_key)

# to summarize
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

def split_into_token_chunks(text: str, max_tokens: int = 300) -> list:
    tokens = text.split()
    chunks = [' '.join(tokens[i:i + max_tokens]) for i in range(0, len(tokens), max_tokens)]
    return chunks

def return_data() -> str:
    return "".join(data)

@dataclass
class SupportDependencies:
    db: str

async def ppt_content(data):
    agent = Agent(model,
                  result_type=customClass.PPT,
                  tools=[return_data],
                  system_prompt=(
                      "You are an expert in making power-point presentations",
                      "Create 5 slides",
                      "Title Slide: short intro about the presentation",
                      "Methodology Slide: Summarize the methodology in detail",
                      "Results Slide: Present key findings in detail in simple text and bullet points.",
                      "Discussion Slide: Summarize the implications and limitations.",
                      "Conclusion Slide: State the overall conclusion.",
                      "Each slide should be separate",
                      "Each slide should have 4 parts:",
                      "1. Title: title of the slide",
                      "2. Text: the precise and short description or narrative content of the slide. This should include key information, explanations, or supporting arguments. Keep it concise yet informative to avoid overwhelming the audience.",
                      "3. Bullet point: A list of bullet points summarizing key information on the slide. Each bullet point should be short, precise, and highlight a specific aspect of the slide's topic. Ideally, limit to 3-5 points.",
                  ))

    listOfString = split_into_token_chunks("".join(data))
    message_history: list[ModelMessage] = []

    if len(listOfString) > 0:
        result = agent.run_sync(user_prompt=f"Create me a powerpoint presentation from {listOfString[0]}",
                                message_history=message_history)
    if len(listOfString) > 1:
        result_1 = agent.run_sync(user_prompt=f"Create me a powerpoint presentation from {listOfString[1]}",
                                  message_history=result.all_messages())
    if len(listOfString) > 2:
        result_2 = agent.run_sync(user_prompt=f"Create me a powerpoint presentation from {listOfString[2]}",
                                  message_history=result_1.all_messages())

    print(result_2.data if len(listOfString) > 2 else result_1.data if len(listOfString) > 1 else result.data)

def ai_ppt(data):
    asyncio.run(ppt_content(data=data))

def extract_data(feed):
    with pdfplumber.open(feed) as pdf:
        pages = pdf.pages
        for p in pages:
            data.append(p.extract_text())
    return None

def main():
    uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf")

    if uploaded_file is not None:
        extract_data(uploaded_file)

        if st.button("Make PPT"):
            ai_ppt(data)

        binary_data = uploaded_file.getvalue()
        pdf_viewer(input=binary_data, width=700)

if __name__ == '__main__':
    import asyncio
    nest_asyncio.apply()
    main()