import asyncio
from pydantic_ai.result import ResultData, 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-3.1-70b-versatile', api_key = api_key)


# to summarize
#summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
#summarizer = pipeline('text2text-generation', model='describeai/gemini')
#nlpaueb/legal-bert-base-uncased




def split_into_token_chunks(text: str, max_tokens: int = 1000) -> list:
    """
    Splits a long string into chunks of a specified maximum number of tokens (words).

    :param text: The input string to split.
    :param max_tokens: The maximum number of tokens (words) per chunk.
    :return: A list of strings, each containing up to `max_tokens` tokens.
    """
    # Split the text into words (tokens)
    tokens = text.split()
    
    # Create chunks of words
    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 perssentation",
        "Create 6 sliders",
        "Title Slide: short into 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.",
        "Reference Slide: Include all citations used.",
        "Each slide should be seperate",
        "Each slide should have 4 parts :"
        "1. Title : title of the slide ",
        "2. Text: he detailed 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 detail, long, and highlight a specific aspect of the slide's topic. ideally, limit to 3-5 points.",
        "4. Image Suggestion: A prompt for generating an image to complement the slide content. Describe the desired visual in detail, including elements, style, and relevance to the topic. Ensure the prompt is actionable for AI tools.", 
    ))
    listOfString = split_into_token_chunks("".join(data))
    print(len(listOfString))
    message_history: list[ModelMessage]  = []
    # for i, chunk in enumerate(listOfString):
    #     print(f"Chunk {i}:\n{chunk}\n")

    for x in listOfString:
        result = agent.run_sync(user_prompt = f"Create me a powerpoint presentation {x}",message_history = message_history)
        message_history = []
        message_history = result.all_messages()
        result_data.append(result.data)
    print(result_data[-1])


    deps :SupportDependencies = SupportDependencies(db="".join(data))
    # while len(listOfString) > 0:

    #     result_1 = agent.run_sync(user_prompt = f"Create me a powerpoint presentation",message_history=message_history)
    # #print(result_1.data)
    #     message_history = result_1.all_messages()
    #     print(result_1)
    

def ai_ppt(data):
    #call summerizer to summerize pdf 
    # summary = summarizer("".join(data), max_length=400, min_length=100, truncation=True,do_sample=False)
    
    # summary_texts = [item['summary_text'] for item in summary]
    #summary_texts = [item['generated_text'] for item in summary]
    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 



# if data is not None:
#     st.caption(data)
#     ai_ppt(data=data)

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()