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",
            "Review this resume and identify areas for improvement in structure, content, and formatting. Suggest specific changes to make it more professional and effective.",
            "Rewrite the following bullet points to make them more impactful, concise, and result-oriented. Use action verbs and quantify achievements wherever possible.",
            "Review this resume for grammar, clarity, and conciseness. Suggest edits to improve readability and professionalism.",
            "Your answer should be a new and improve resume 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()