Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import tempfile | |
| import os | |
| from inference import predict_from_pdf | |
| # ========================= | |
| # PAGE CONFIG | |
| # ========================= | |
| st.set_page_config( | |
| page_title="AI Resume Category Classifier", | |
| page_icon="📄", | |
| layout="centered" | |
| ) | |
| # ========================= | |
| # SIDEBAR | |
| # ========================= | |
| st.sidebar.title("📄 Resume Parser – ML App") | |
| st.sidebar.write( | |
| """ | |
| This app uses a trained **machine learning model** to | |
| classify resumes into **job categories** based on their content. | |
| """ | |
| ) | |
| st.sidebar.markdown("---") | |
| st.sidebar.caption("Built as an internship project and refined for college submission.") | |
| # ========================= | |
| # MAIN CONTENT | |
| # ========================= | |
| st.title("📄 AI Resume Category Classifier") | |
| st.write( | |
| """ | |
| Upload a **PDF resume** and the model will predict its **category/domain**. | |
| """ | |
| ) | |
| uploaded_file = st.file_uploader( | |
| "Upload a resume (PDF file)", | |
| type=["pdf"], | |
| accept_multiple_files=False | |
| ) | |
| if uploaded_file is not None: | |
| st.info(f"Uploaded file: `{uploaded_file.name}`") | |
| if st.button("🔍 Analyze Resume"): | |
| with st.spinner("Reading and classifying the resume..."): | |
| # Save uploaded file to a temporary path | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp: | |
| tmp.write(uploaded_file.read()) | |
| temp_pdf_path = tmp.name | |
| try: | |
| predicted_label, proba_dict, raw_text = predict_from_pdf(temp_pdf_path) | |
| # Show predicted category | |
| st.success(f"Predicted Category: **{predicted_label}**") | |
| # Show probabilities (if available) | |
| if proba_dict is not None: | |
| st.subheader("Prediction Confidence") | |
| sorted_items = sorted(proba_dict.items(), key=lambda x: x[1], reverse=True) | |
| for label, prob in sorted_items: | |
| st.write(f"- {label}: `{prob:.2%}`") | |
| # Show extracted text preview | |
| with st.expander("🔎 View extracted resume text (preview)"): | |
| preview = (raw_text or "").strip() | |
| if preview: | |
| st.text(preview[:2000]) # first 2000 characters | |
| else: | |
| st.write("No text could be extracted from the PDF.") | |
| except Exception as e: | |
| st.error(f"❌ Error while processing the file: {e}") | |
| finally: | |
| # Clean up temp file | |
| if os.path.exists(temp_pdf_path): | |
| os.remove(temp_pdf_path) | |
| else: | |
| st.warning("Please upload a PDF resume to start.") | |