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import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from convert import ExtractPDFText
from ATS_score import calculateATSscore, skill_gap_analysis
from model import modelFeedback
import time
# Streamlit app title
st.title("Resume Screening Assistance")
# Define required skills for the job
required_skills = ["Python", "Machine Learning", "Cloud (AWS/Azure)", "Data Analysis", "React", "Docker"]
# Job Description Input
job_description = st.text_area("Paste the job description below:", placeholder="Enter the job description here...")
# Upload Resumes
uploaded_files = st.file_uploader("Upload your resumes (PDF only):", type="pdf", accept_multiple_files=True)
# Add a submit button
if st.button("Submit"):
if uploaded_files and job_description:
resumes_data = []
# Initialize progress bar
progress_bar = st.progress(0)
total_resumes = len(uploaded_files)
# Placeholder to show the current processing file
current_file_placeholder = st.empty()
for idx, file in enumerate(uploaded_files, start=1):
try:
# Update the current file being processed in the placeholder
current_file_placeholder.text(f"Currently processing: {file.name}")
# Extract resume text
text = ExtractPDFText(file)
# Calculate ATS score and skill gap analysis
ats_score = calculateATSscore(text, job_description)
skill_analysis = skill_gap_analysis(text, required_skills)
# Generate AI feedback
feedback = modelFeedback(ats_score, text, job_description)
# Append results for each resume
resumes_data.append({
"name": file.name,
"ATS Score": ats_score,
"Skills Present": skill_analysis["present"],
"Missing Skills": skill_analysis["missing"],
"Feedback": feedback
})
# Update progress bar
progress_bar.progress(idx / total_resumes)
except Exception as e:
st.error(f"Error processing {file.name}: {e}")
# Clear the current file placeholder after processing all files
current_file_placeholder.empty()
if resumes_data:
# Sort resumes by ATS score
sorted_resumes = sorted(resumes_data, key=lambda x: x["ATS Score"], reverse=True)
# Add rankings
for rank, resume in enumerate(sorted_resumes, start=1):
resume["Rank"] = rank
# Display results
st.subheader("Top Matches:")
for resume in sorted_resumes:
st.write(f"#### Rank {resume['Rank']}: {resume['name']}")
st.write(f"**ATS Score:** {resume['ATS Score']*100:.0f}%")
st.write("**Missing Skills:**", ", ".join(resume["Missing Skills"]) if resume["Missing Skills"] else "None")
st.write("**Feedback:**")
st.markdown(f"> {resume['Feedback']}")
st.write("---")
# Skill Gap Analysis Table
st.subheader("Skill Gap Analysis")
for resume in sorted_resumes:
st.write(f"**{resume['name']} (Rank {resume['Rank']})**")
skill_data = {
"Required Skills": required_skills,
"Present in Resume": ["✅" if skill in resume["Skills Present"] else "❌" for skill in required_skills],
"Missing": [skill if skill in resume["Missing Skills"] else "—" for skill in required_skills]
}
df = pd.DataFrame(skill_data)
st.table(df)
# Visualize ATS Scores with Rankings
st.subheader("ATS Score Distribution")
names_with_ranks = [f"Rank {r['Rank']}: {r['name']}" for r in sorted_resumes]
scores = [r["ATS Score"]*100 for r in sorted_resumes]
plt.figure(figsize=(8, 5))
plt.bar(names_with_ranks, scores, color='skyblue')
plt.title("ATS Scores by Resume (Ranked)")
plt.ylabel("ATS Score (%)")
plt.xticks(rotation=45, ha="right")
st.pyplot(plt)
else:
st.error("No resumes could be processed. Please check the uploaded files.")
else:
st.warning("Please upload resumes and provide a job description before submitting.")
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