app.py

# -*- coding: utf-8 -*-
import streamlit as st
import os
import pandas as pd
import matplotlib.pyplot as plt
from resume_generation_gemini_pro import Gemini_pro_main
from similarity_score_refined import similarity_main

# Helper function to save uploaded files temporarily and return their paths
def save_uploaded_file(uploaded_file):
    file_path = os.path.join("/tmp", uploaded_file.name)
    with open(file_path, "wb") as f:
        f.write(uploaded_file.getbuffer())
    return file_path

# Custom CSS for styling
st.markdown("""
    <style>
        .main {
            background-color: #f5f5f5;
            font-family: Arial, sans-serif;
        }
        h1, h2 {
            color: #4B7BE5;
            text-align: center;
        }
        .stButton>button {
            background-color: #4B7BE5;
            color: white;
            font-size: 18px;
        }
        .stButton>button:hover {
            background-color: #3A6FD8;
            color: white;
        }
    </style>
""", unsafe_allow_html=True)

# Title and Description
st.title("Resume Tailoring with Google Generative AI")
st.markdown("### Upload your resume and job description to check similarity and generate a tailored resume.")

# Two columns for file uploaders
col1, col2 = st.columns(2)
with col1:
    uploaded_resume = st.file_uploader("Upload Current Resume (.docx or .pdf)", type=["docx", "pdf"], key="resume")
with col2:
    uploaded_job_description = st.file_uploader("Upload Job Description (.docx or .pdf)", type=["docx", "pdf"], key="job_description")

# Process if files are uploaded
if uploaded_resume and uploaded_job_description:
    # Save files
    resume_path = save_uploaded_file(uploaded_resume)
    job_description_path = save_uploaded_file(uploaded_job_description)

    # Similarity Score Section
    st.markdown("---")
    st.subheader("Check Resume Similarity")

    if st.button("Check Similarity Score"):
        similarity_score = similarity_main(resume_path, job_description_path)
        if isinstance(similarity_score, str) and '%' in similarity_score:
            similarity_score = float(similarity_score.replace('%', ''))

        # Display Score as a Pie Chart
        st.markdown(f"### Similarity Score: {int(similarity_score)}%")
        
        # Pie chart to show similarity
        fig, ax = plt.subplots()
        ax.pie([similarity_score, 100 - similarity_score], labels=['Match', 'Difference'], autopct='%1.1f%%', startangle=140, colors=['#4B7BE5', '#E5E5E5'])
        ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
        st.pyplot(fig)

    # Generate Tailored Resume Section
    st.markdown("---")
    st.subheader("Generate Tailored Resume")

    if st.button("Generate Tailored Resume"):
        with st.spinner("Generating resume..."):
            generated_resume = Gemini_pro_main(resume_path, job_description_path)
            st.subheader("Generated Tailored Resume:")
            st.write(generated_resume)

else:
    st.warning("Please upload both the resume and job description files.")