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import asyncio |
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import re |
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from pydantic_ai.result import ResultData, RunResult |
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import streamlit as st |
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from pydantic_ai import Agent,RunContext, Tool |
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from pydantic_ai.models.groq import GroqModel |
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import nest_asyncio |
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from pydantic_ai.messages import ModelMessage |
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import pdfplumber |
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import os |
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from streamlit_pdf_viewer import pdf_viewer |
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from dataclasses import dataclass |
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api_key = os.getenv("api_key") |
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data = [] |
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model = GroqModel("llama3-groq-70b-8192-tool-use-preview", api_key = api_key) |
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async def resume_AI(data): |
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agent = Agent(model=model, |
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system_prompt=( |
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"You are an expert in making resume", |
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"You have access to the resume text", |
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"Also return data in markdown formate" |
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) |
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) |
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result = agent.run_sync(user_prompt=f"Improve this resume: {data}") |
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print(result.data) |
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def extract_data(feed): |
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with pdfplumber.open(feed) as pdf: |
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pages = pdf.pages |
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for p in pages: |
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data.append(p.extract_text()) |
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return None |
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def ai_resume(data): |
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asyncio.run(resume_AI(data=data)) |
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def main(): |
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uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf") |
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if uploaded_file is not None: |
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extract_data(uploaded_file) |
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binary_data = uploaded_file.getvalue() |
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pdf_viewer(input=binary_data, |
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width=700) |
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if st.button("Improve Resume"): |
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ai_resume(data) |
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if __name__ == '__main__': |
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import asyncio |
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nest_asyncio.apply() |
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main() |
