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 = []
user_input = ""


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

async def resume_AI(data):
    agent = Agent(model=model,
        system_prompt=(
            "Act as a professional resume editor and career consultant. Analyze the provided resume and improve it",
            "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.",
            "Rewrite my resume to be more concise and impactful",
            "Highlight my key achievements and skills.",
            "Use strong action verbs and quantify my accomplishments whenever possible.",
            "Correct any grammar, spelling, or punctuation errors.",
            "Improve sentence structure for clarity and professionalism.",
            "Incorporate industry-specific keywords and phrases to make the resume ATS-friendly.",
            "Create a compelling summary/profile section that reflects my career goals.",
           # "Incorporate keywords relevant to the target industry and roles",
            #"Optimize the resume for Applicant Tracking Systems (ATS)."
            "Your answer should be a new and improve resume in markdown formate",
            #"Answer should be in markdown formate"
        )
        
    )

    print(data)
    result = agent.run_sync(user_prompt=f"Improve this resume: {data}")
    st.markdown(result.data)

def extract_data(feed):
    
    with pdfplumber.open(feed) as pdf:
        pages = pdf.pages
        for p in pages:
            print(p.extract_words)
            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")
    user_input = st.text_area(label="Enter job detail")
    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()