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import streamlit as st
from pdf_to_image import pdf_to_image
from image_to_text import image_to_text
from mirascope_extractor import extractor
import google.generativeai as genai
import pandas as pd
import glob
import os
# from dotenv import load_dotenv
import streamlit as st
from resume_recommender import recommend_resume
# load_dotenv()
global empty_df
openai_api_key = os.environ['OPENAI_API_KEY']
print('This is API key: ', openai_api_key)
genai.configure(api_key=openai_api_key)
st.set_page_config(page_title="CV Recommendor Gen AI")
st.title("Gen AI CV Recommender")
st.write("""
**Find the Best Candidates for Your Job!**
🔍 **What It Does:**
- Analyzes multiple resumes in PDF format.
- Matches them with your job description.
📋 **How It Works:**
1. Upload resumes 📄
2. Upload the job description 📝
3. Get a table of top recommended candidates 🏆
Ready to find the perfect fit? Let's get started!
""")
uploaded_files = st.sidebar.file_uploader("Choose PDF files", accept_multiple_files=True, type="pdf")
job_description = st.sidebar.text_input('Enter Job Description')
if uploaded_files and job_description:
if st.sidebar.button('AI Recommendation'):
image_bytes = pdf_to_image(uploaded_files)
all_texts = []
for image_byte in image_bytes:
print('This is image_byte: ', image_byte)
combine_text = ''
for image in image_byte:
text = image_to_text(image)
combine_text += text
print('This is the text from single PDF: ', combine_text)
all_texts.append(combine_text)
empty_df = pd.DataFrame()
for text in all_texts:
extracted_text = extractor(text)
task_details_dict = extracted_text.dict()
df = pd.DataFrame([task_details_dict])
empty_df = pd.concat([empty_df, df])
recommend_df = recommend_resume(empty_df, job_description)
if 'Unnamed: 0' in recommend_df.columns:
recommend_df = recommend_df.drop('Unnamed: 0', axis=1)
print('Recommendation process done successfully')
st.write(recommend_df)
# csv = empty_df.to_csv(index=False)
# st.download_button(
# label = 'Click to Download CSV',
# data = csv,
# file_name = 'Extracted_data.csv',
# mime='text/csv',
# )
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