File size: 3,061 Bytes
92adbc4
 
 
787c07d
2880f9e
787c07d
9b9d05d
92adbc4
 
 
 
 
 
 
 
787c07d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
92adbc4
787c07d
92adbc4
787c07d
 
 
 
 
 
92adbc4
787c07d
 
 
92adbc4
 
 
787c07d
 
 
 
9b9d05d
 
02f7cad
787c07d
02f7cad
2880f9e
787c07d
2880f9e
 
 
 
 
 
9b9d05d
787c07d
 
 
92adbc4
787c07d
 
 
9b9d05d
 
787c07d
92adbc4
787c07d
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
# -*- 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.")