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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances | |
| from sklearn.preprocessing import MinMaxScaler | |
| import re | |
| from PyPDF2 import PdfReader | |
| def extract_text_from_file(file): | |
| if file.type == "application/pdf": | |
| return extract_text_from_pdf(file) | |
| else: | |
| return file.read().decode('utf-8') | |
| def extract_text_from_pdf(file): | |
| reader = PdfReader(file) | |
| text = "" | |
| for page in reader.pages: | |
| text += page.extract_text() | |
| return text | |
| def clean_text(text): | |
| text = re.sub(r'\W', ' ', text) | |
| return text.lower() | |
| def calculate_similarity_metrics(resumes, keywords): | |
| tfidf_vectorizer = TfidfVectorizer() | |
| tfidf_matrix = tfidf_vectorizer.fit_transform(resumes + [keywords]) | |
| cosine_sim = cosine_similarity(tfidf_matrix[-1], tfidf_matrix[:-1]).flatten() | |
| def jaccard_similarity(doc1, doc2): | |
| set1 = set(doc1.split()) | |
| set2 = set(doc2.split()) | |
| return len(set1.intersection(set2)) / len(set1.union(set2)) | |
| jaccard_sim = [jaccard_similarity(keywords, resume) for resume in resumes] | |
| euclidean_dist = euclidean_distances(tfidf_matrix[-1], tfidf_matrix[:-1]).flatten() | |
| euclidean_sim = 1 / (1 + euclidean_dist) | |
| return cosine_sim, jaccard_sim, euclidean_sim | |
| st.title("Resume Analyzer") | |
| st.sidebar.subheader("Enter Keywords and Priority") | |
| data = pd.DataFrame({ | |
| 'Keyword': ['']*10, | |
| 'Priority': ['']*10 | |
| }) | |
| keywords_df = st.sidebar.data_editor(data, num_rows="dynamic", key="keyword_table") | |
| if not keywords_df['Keyword'].isnull().all(): | |
| keywords_combined = " ".join(keywords_df.apply(lambda row: f"{row['Keyword']} " * int(row['Priority']) if row['Priority'].isdigit() else row['Keyword'], axis=1)) | |
| st.subheader("Upload up to 5 resumes (PDF or Text files)") | |
| uploaded_files = st.file_uploader("Choose Resume Files", accept_multiple_files=True, type=["txt", "pdf"]) | |
| if len(uploaded_files) > 0 and keywords_combined: | |
| with st.spinner("Analyzing Resumes..."): | |
| resumes = [] | |
| for file in uploaded_files: | |
| try: | |
| resume_text = extract_text_from_file(file) | |
| clean_resume = clean_text(resume_text) | |
| resumes.append(clean_resume) | |
| except Exception as e: | |
| st.error(f"Error processing {file.name}: {str(e)}") | |
| clean_keywords = clean_text(keywords_combined) | |
| cosine_scores, jaccard_scores, euclidean_scores = calculate_similarity_metrics(resumes, clean_keywords) | |
| st.subheader("Resume Analysis Results") | |
| results_df = pd.DataFrame({ | |
| 'Resume': [file.name for file in uploaded_files], | |
| 'Cosine Similarity': cosine_scores, | |
| 'Jaccard Index': jaccard_scores, | |
| 'Euclidean Similarity': euclidean_scores | |
| }) | |
| scaler = MinMaxScaler() | |
| normalized_scores = scaler.fit_transform(results_df[['Cosine Similarity', 'Jaccard Index', 'Euclidean Similarity']]) | |
| overall_scores = np.mean(normalized_scores, axis=1) | |
| results_df['Overall Score'] = overall_scores | |
| results_df['Rank'] = results_df['Overall Score'].rank(ascending=False, method='min').astype(int) | |
| results_df = results_df.sort_values('Rank') | |
| st.dataframe(results_df) | |
| else: | |
| st.info("Please upload resumes and enter keywords with priority.") |