Create app.py

app.py

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+import streamlit as st
+from tika import parser
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.multiclass import OneVsRestClassifier
+from sklearn.neighbors import KNeighborsClassifier
+import re
+import pickle
+
+def cleanResume(resumeText):
+    # Your existing cleanResume function remains unchanged
+    resumeText = re.sub('http\S+\s*', ' ', resumeText)
+    resumeText = re.sub('RT|cc', ' ', resumeText)
+    resumeText = re.sub('#\S+', '', resumeText)
+    resumeText = re.sub('@\S+', '  ', resumeText)
+    resumeText = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), ' ', resumeText)
+    resumeText = re.sub(r'[^\x00-\x7f]',r' ', resumeText)
+    resumeText = re.sub('\s+', ' ', resumeText)
+    return resumeText
+
+df = pd.read_csv('UpdatedResumeDataSet.csv')
+df['cleaned'] = df['Resume'].apply(lambda x: cleanResume(x))
+label = LabelEncoder()
+df['Category'] = label.fit_transform(df['Category'])
+text = df['cleaned'].values
+target = df['Category'].values
+word_vectorizer = TfidfVectorizer(
+    sublinear_tf=True,
+    stop_words='english',
+    max_features=1500)
+word_vectorizer.fit(text)
+WordFeatures = word_vectorizer.transform(text)
+model = OneVsRestClassifier(KNeighborsClassifier())
+model.fit(WordFeatures, target)
+
+def pdf_to_text(file):
+    # Use tika to extract text from the PDF file
+    file_data = parser.from_buffer(file.read())
+    text = file_data['content']
+    return text
+
+def predict_category(resumes_data, selected_category):
+    resumes_df = pd.DataFrame(resumes_data)
+    resumes_features = word_vectorizer.transform(resumes_df['ResumeText'])
+    predicted_probs = model.predict_proba(resumes_features)
+
+    # Assign probabilities to respective job categories
+    for i, category in enumerate(label.classes_):
+        resumes_df[category] = predicted_probs[:, i]
+
+    resumes_df_sorted = resumes_df.sort_values(by=selected_category, ascending=False)
+
+    # Get the ranks for the selected category
+    ranks = []
+    for rank, (idx, row) in enumerate(resumes_df_sorted.iterrows()):
+        rank = rank + 1
+        file_name = row['FileName']
+        ranks.append({'Rank': rank, 'FileName': file_name})
+
+    return ranks
+
+def main():
+    st.title("Resume Ranking App")
+    st.text("Upload resumes and select a category to rank them.")
+
+    resumes_data = []
+    selected_category = ""
+
+    # Handle multiple file uploads
+    files = st.file_uploader("Upload resumes", type=["pdf"], accept_multiple_files=True)
+    if files:
+        for file in files:
+            text = cleanResume(pdf_to_text(file))
+            resumes_data.append({'ResumeText': text, 'FileName': file.name})
+
+        selected_category = st.selectbox("Select a category to rank by", label.classes_)
+
+    if st.button("Rank Resumes"):
+        if not resumes_data or not selected_category:
+            st.warning("Please upload resumes and select a category to continue.")
+        else:
+            ranks = predict_category(resumes_data, selected_category)
+            st.write(pd.DataFrame(ranks))
+
+if __name__ == '__main__':
+    main()