import asyncio
import re
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
import os
from streamlit_pdf_viewer import pdf_viewer
from dataclasses import dataclass

#api_key
#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

api_key = os.getenv("api_key")

data = []

model = GroqModel("llama3-groq-70b-8192-tool-use-preview", api_key = api_key)

async def resume_AI(data):
    agent = Agent(model=model,
    
        system_prompt=(
            "You are an expert in making resume",
            "You have access to the resume text",
            "Also return data in markdown formate"
        )
        
    )

    result = agent.run_sync(user_prompt=f"Improve this resume: {data}")
    print(result.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 ai_resume(data):
    asyncio.run(resume_AI(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)
        binary_data = uploaded_file.getvalue()
        pdf_viewer(input=binary_data,
                width=700)
    if st.button("Improve Resume"):
        ai_resume(data)



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