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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() |