Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+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_cosine_similarity(resumes, keywords):
+    tfidf_vectorizer = TfidfVectorizer()
+    tfidf_matrix = tfidf_vectorizer.fit_transform(resumes + [keywords])
+    cosine_sim = cosine_similarity(tfidf_matrix[-1], tfidf_matrix[:-1])
+    return cosine_sim.flatten()
+
+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)
+            
+            scores = calculate_cosine_similarity(resumes, clean_keywords)
+            
+            st.subheader("Resume Analysis Results")
+            results_df = pd.DataFrame({
+                'Resume': [file.name for file in uploaded_files],
+                'Similarity Score': scores
+            })
+            st.dataframe(results_df)
+else:
+    st.info("Please upload resumes and enter keywords with priority.")