app.py

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

# Load API key
api_key = os.getenv("API_KEY")
if not api_key:
    raise ValueError("API_KEY is not set in the environment variables.")

data = []
result_data: list[customClass.PPT] = []

# Initialize models
model = GroqModel("llama3-groq-70b-8192-tool-use-preview", api_key=api_key)
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

def split_into_token_chunks(text: str, max_tokens: int = 300) -> list:
    """
    Splits a long string into chunks of a specified maximum number of tokens (words).
    """
    tokens = text.split()
    return [' '.join(tokens[i:i + max_tokens]) for i in range(0, len(tokens), max_tokens)]

def return_data() -> str:
    """Returns concatenated extracted data."""
    return "\n".join(data)

@dataclass
class SupportDependencies:
    db: str

async def ppt_content(data):
    """
    Generates PowerPoint content using an AI model.
    """
    if not data:
        raise ValueError("No valid text found for PowerPoint generation.")

    agent = Agent(
        model,
        result_type=customClass.PPT,
        tools=[return_data],
        system_prompt="""
        You are an expert in creating PowerPoint presentations.
        Create 5 slides:
        1. Title Slide: Introduction about the presentation.
        2. Methodology Slide: Summarize the methodology in detail.
        3. Results Slide: Present key findings in bullet points.
        4. Discussion Slide: Summarize implications and limitations.
        5. Conclusion Slide: State the overall conclusion.
        
        Each slide should have:
        - Title: Clear and concise.
        - Text: Short and informative explanation.
        - Bullet Points: 3-5 summarized key takeaways.
        """
    )

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

    result = agent.run_sync(user_prompt=f"Create a PowerPoint presentation from {listOfString[0]}", message_history=message_history)
    
    for i in range(1, len(listOfString)):
        result = agent.run_sync(user_prompt=f"Continue creating the PowerPoint presentation from {listOfString[i]}", message_history=result.all_messages())

    print(result.data)

def ai_ppt(data):
    """Runs the PowerPoint generation in an async loop."""
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    loop.run_until_complete(ppt_content(data=data))

def extract_data(feed):
    """Extracts text from PDF and appends to `data` list."""
    global data
    data = []  # Reset data before extracting
    with pdfplumber.open(feed) as pdf:
        for p in pdf.pages:
            text = p.extract_text()
            if text:
                data.append(text)

def main():
    """Main Streamlit app function."""
    st.title("AI-Powered PowerPoint Generator")
    
    uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
    
    if uploaded_file is not None:
        extract_data(uploaded_file)
        
        if st.button("Generate PPT"):
            try:
                ai_ppt(data)
                st.success("PowerPoint generation completed!")
            except Exception as e:
                st.error(f"Error generating PPT: {e}")

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

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