Update app.py

app.py

@@ -1,77 +1,77 @@
-import streamlit as st
-import pickle
-import re
-import nltk
-
-nltk.download('punkt')
-nltk.download('stopwords')
-
-#loading models
-clf = pickle.load(open('clf.pkl','rb'))
-tfidfd = pickle.load(open('tfidf.pkl','rb'))
-
-def clean_resume(resume_text):
-    clean_text = re.sub('http\S+\s*', ' ', resume_text)
-    clean_text = re.sub('RT|cc', ' ', clean_text)
-    clean_text = re.sub('#\S+', '', clean_text)
-    clean_text = re.sub('@\S+', '  ', clean_text)
-    clean_text = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), ' ', clean_text)
-    clean_text = re.sub(r'[^\x00-\x7f]', r' ', clean_text)
-    clean_text = re.sub('\s+', ' ', clean_text)
-    return clean_text
-# web app
-def main():
-    st.title("Resume Screening App")
-    uploaded_file = st.file_uploader('Upload Resume', type=['txt','pdf'])
-
-    if uploaded_file is not None:
-        try:
-            resume_bytes = uploaded_file.read()
-            resume_text = resume_bytes.decode('utf-8')
-        except UnicodeDecodeError:
-            # If UTF-8 decoding fails, try decoding with 'latin-1'
-            resume_text = resume_bytes.decode('latin-1')
-
-        cleaned_resume = clean_resume(resume_text)
-        input_features = tfidfd.transform([cleaned_resume])
-        prediction_id = clf.predict(input_features)[0]
-        st.write(prediction_id)
-
-        # Map category ID to category name
-        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("Predicted Category:", category_name)
-
-
-
-# python main
-if __name__ == "__main__":
-    main()
+import streamlit as st
+import pickle
+import re
+import nltk
+
+nltk.download('punkt')
+nltk.download('stopwords')
+
+#loading models
+clf = pickle.load(open('clf.pkl','rb'))
+tfidfd = pickle.load(open('tfidf.pkl','rb'))
+
+def clean_resume(resume_text):
+    clean_text = re.sub('http\S+\s*', ' ', resume_text)
+    clean_text = re.sub('RT|cc', ' ', clean_text)
+    clean_text = re.sub('#\S+', '', clean_text)
+    clean_text = re.sub('@\S+', '  ', clean_text)
+    clean_text = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), ' ', clean_text)
+    clean_text = re.sub(r'[^\x00-\x7f]', r' ', clean_text)
+    clean_text = re.sub('\s+', ' ', clean_text)
+    return clean_text
+# web app
+def main():
+    st.title("Resume Screening App")
+    uploaded_file = st.file_uploader('Upload Resume', type=['txt','pdf'])
+
+    if uploaded_file is not None:
+        try:
+            resume_bytes = uploaded_file.read()
+            resume_text = resume_bytes.decode('utf-8')
+        except UnicodeDecodeError:
+            # If UTF-8 decoding fails, try decoding with 'latin-1'
+            resume_text = resume_bytes.decode('latin-1')
+
+        cleaned_resume = clean_resume(resume_text)
+        input_features = tfidfd.transform([cleaned_resume])
+        prediction_id = clf.predict(input_features)[0]
+        st.write(prediction_id)
+
+        # Map category ID to category name
+        category_mapping = {
+            15: "Java Developer",
+            23: "Testing",
+            8: "AI/ML 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("Predicted Category:", category_name)
+
+
+
+# python main
+if __name__ == "__main__":
+    main()