Update app.py

app.py

@@ -1,25 +1,68 @@
-import numpy as np
-from flask import Flask, request, render_template
+import streamlit as st
 import pickle
+import re
+import nltk
 
-flask_app = Flask(__name__)
+nltk.download('punkt')
+nltk.stopwords('stopwords')
 
-model = pickle.load(open("model.pkl", "rb"))
+model = pickle.load(open('model.pkl','rb'))
+tfidf = pickle.load(open('tfidf.pkl','rb'))
 
-@flask_app.route("/")
-def Home():
-    return render_template("index.html")
+def clean_resume(resume_text):
+        cleanText = re.sub('http\S+\s', " ", txt)
+        cleanText = re.sub('RT|cc', ' ', cleanText)
+        cleanText = re.sub("#\S+\s", ' ', cleanText)
+        cleanText = re.sub('@\S+\s', ' ', cleanText)
+        cleanText = re.sub('[%s]' % re.escape("""!#$%&'()*+-,":/\;<=>?_[]^{}~`"""), ' ', cleanText)
+        cleanText = re.sub(r'[^\x00-\x7f]', " ", cleanText)
+        return cleanText
 
-@flask_app.route("/predict", methods=["POST"])
-def predict():
-    float_features = [float(x) for x in request.form.values()]
-    features = [np.array(float_features)]
-    prediction = model.predict(features)
-    if prediction=='B':
-        return render_template("index.html", prediction_text="the person was not classified with cancer")
-    else:
-        return render_template("index.html", prediction_text="the Cancer was detected in the patient ")
+def main():
+    st.title("Resume Screening Application")
+    uploaded_file = st.file_uploader("Upload Resume Here",type=['txt','pdf'])
+    if uploaded_file is not None:
+        try:
+            resume_bytes = uploaded_file.read()
+            resume_text = resume_bytes.decode('utf-8')
+        except:
+            resume_text = resume_bytes.decode('latin-1')
 
+        cleaned_resume = clean_resume(resume_text)
+        input_features  = tfidf.transform([cleaned_resume])
+        prediction_id = model.predict(input_features)[0]
+        st.write(prediction_id)
+
+        category_mapping = {
+            15: "Java Developer",
+            23: "Testing",
+            8: "DevOps Engineer",
+            20: "Python Developer",
+            24: "Web Designing",
+            12: "HR",
+            13: "Hadoop",
+            3: "Blockchain",
+            10: "ETL Developer",
+            18: "Operations Manager",
+            6: "Data Science",
+            22: "Sales",
+            16: "Mechanical Engineer",
+            1: "Arts",
+            7: "Database",
+            11: "Electrical Engineering",
+            14: "Health and fitness",
+            19: "PMO",
+            4: "Business Analyst",
+            9: "DotNet Developer",
+            2: "Automation Testing",
+            17: "Network Security Engineer",
+            21: "SAP Developer",
+            5: "Civil Engineer",
+            0: "Advocate",
+        }
+
+        category_name = category_mapping.get(prediction_id,'Unknown')
+        st.write("THE PREDICTED CATEGORY IS: ",category_name)
 
 if __name__ == '__main__':
-    flask_app.run(debug=True)
+    main()