Update app.py

Added streamlit

app.py

@@ -1,24 +1,27 @@
-import gradio as gr
+import streamlit as st
 import pickle
 import docx
 import PyPDF2
 import re
+from PIL import Image
 
 # Load pre-trained model and TF-IDF vectorizer
 svc_model = pickle.load(open('clf.pkl', 'rb'))  # Update with your model path
 tfidf = pickle.load(open('tfidf.pkl', 'rb'))  # Update with your vectorizer path
 le = pickle.load(open('encoder.pkl', 'rb'))  # Update with your encoder path
 
+
 # Function to clean resume text
-def clean_resume(txt):
-    clean_text = re.sub('http\S+\s', ' ', txt)
-    clean_text = re.sub('RT|cc', ' ', clean_text)
-    clean_text = re.sub('#\S+\s', ' ', clean_text)
-    clean_text = re.sub('@\S+', '  ', clean_text)
-    clean_text = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~"""), ' ', clean_text)
-    clean_text = re.sub(r'[^\x00-\x7f]', ' ', clean_text)
-    clean_text = re.sub('\s+', ' ', clean_text)
-    return clean_text
+def cleanResume(txt):
+    cleanText = re.sub('http\S+\s', ' ', txt)
+    cleanText = re.sub('RT|cc', ' ', cleanText)
+    cleanText = re.sub('#\S+\s', ' ', cleanText)
+    cleanText = re.sub('@\S+', '  ', cleanText)
+    cleanText = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), ' ', cleanText)
+    cleanText = re.sub(r'[^\x00-\x7f]', ' ', cleanText)
+    cleanText = re.sub('\s+', ' ', cleanText)
+    return cleanText
+
 
 # Function to extract text from PDF
 def extract_text_from_pdf(file):
@@ -28,6 +31,7 @@ def extract_text_from_pdf(file):
         text += page.extract_text()
     return text
 
+
 # Function to extract text from DOCX
 def extract_text_from_docx(file):
     doc = docx.Document(file)
@@ -36,6 +40,7 @@ def extract_text_from_docx(file):
         text += paragraph.text + '\n'
     return text
 
+
 # Function to extract text from TXT
 def extract_text_from_txt(file):
     try:
@@ -44,6 +49,7 @@ def extract_text_from_txt(file):
         text = file.read().decode('latin-1')
     return text
 
+
 # Function to handle file upload and extraction
 def handle_file_upload(uploaded_file):
     file_extension = uploaded_file.name.split('.')[-1].lower()
@@ -57,27 +63,71 @@ def handle_file_upload(uploaded_file):
         raise ValueError("Unsupported file type. Please upload a PDF, DOCX, or TXT file.")
     return text
 
+
 # Function to predict the category of a resume
-def predict_category(file):
-    try:
-        resume_text = handle_file_upload(file)
-        cleaned_text = clean_resume(resume_text)
-        vectorized_text = tfidf.transform([cleaned_text])
-        vectorized_text = vectorized_text.toarray()
-        predicted_category = svc_model.predict(vectorized_text)
-        predicted_category_name = le.inverse_transform(predicted_category)
-        return f"Predicted Category: {predicted_category_name[0]}"
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-# Define Gradio interface
-inputs = gr.File(label="Upload Resume (PDF, DOCX, TXT)")
-outputs = gr.Textbox(label="Prediction")
-
-interface = gr.Interface(fn=predict_category, inputs=inputs, outputs=outputs, title="Resume Classifier",
-                         description="Upload your resume to predict its job category using an AI model.")
-
-# Launch the interface
-if __name__ == "__main__":
-    interface.launch(share=True)
+def pred(input_resume):
+    cleaned_text = cleanResume(input_resume)
+    vectorized_text = tfidf.transform([cleaned_text])
+    vectorized_text = vectorized_text.toarray()
+    predicted_category = svc_model.predict(vectorized_text)
+    predicted_category_name = le.inverse_transform(predicted_category)
+    return predicted_category_name[0]
+
+
+def main():
 
+    st.set_page_config(page_title="Resume Classifier", page_icon="📄", layout="wide")
+
+    # Sidebar design
+    # st.sidebar.image("sidebar_logo.png", use_column_width=True)  # Add your sidebar logo
+    st.sidebar.title("Navigation")
+    st.sidebar.write("👋 Welcome to the Resume Classifier!")
+    st.sidebar.info("Use this tool to predict the category of resumes.")
+    st.sidebar.markdown("---")
+    st.sidebar.header("Instructions")
+    st.sidebar.write("1. Upload a resume file (PDF, DOCX, or TXT).")
+    st.sidebar.write("2. View the extracted resume text.")
+    st.sidebar.write("3. Get the predicted job category.")
+
+    # Main page
+    st.title("📄 Resume Classifier")
+    st.markdown("Upload your resume and get an AI-powered prediction of the job category.")
+
+    # File upload
+    uploaded_file = st.file_uploader("Upload a Resume", type=["pdf", "docx", "txt"])
+    if uploaded_file is not None:
+        try:
+            resume_text = handle_file_upload(uploaded_file)
+            st.success("Successfully extracted the text from the uploaded resume.")
+            
+            # Display extracted text
+            with st.expander("View Extracted Text"):
+                st.text_area("Extracted Resume Text", resume_text, height=300)
+
+            # Display prediction
+            st.subheader("Predicted Category")
+            category = pred(resume_text)
+            st.write(f"The predicted category is: **{category}**")
+
+        except Exception as e:
+            st.error(f"Error: {str(e)}")
+
+    # Footer with copyright and developer credits
+    st.markdown("---")
+    col1, col2 = st.columns([1, 3])
+
+   
+
+    with col2:
+        st.markdown(
+            """
+            <p style='text-align: center;'>
+            &copy; 2025 Resume Classifier. All rights reserved.<br>
+            Developed with ❤️ by <a href="https://github.com/" target="_blank">Aashish</a>.
+            </p>
+            """,
+            unsafe_allow_html=True
+        )
+
+if __name__ == "__main__":
+    main()