Update app.py

app.py

@@ -3,7 +3,7 @@ import joblib
 import numpy as np
 from sklearn.feature_extraction.text import CountVectorizer
 
-# Load models (ensure these models are pre-trained and saved in .pkl files)
+# Load models (change paths to where your .pkl files are stored)
 logreg_model = joblib.load('best_lr_model.pkl')
 knn_model = joblib.load('best_knn_model.pkl')
 svc_model = joblib.load('best_svc_model.pkl')  # Ensure this model has 'probability=True' if needed
@@ -15,7 +15,7 @@ vectorizer = joblib.load('vectorizer.pkl')
 # Define the function to predict sentiment
 def predict_sentiment(review_text, model_name):
     # Preprocess the review text using the same vectorizer used during training
-    transformed_text = vectorizer.transform([review_text])  # Transform the input review text
+    transformed_text = vectorizer.transform([review_text])  # This is sparse matrix
 
     # Convert sparse matrix to dense (necessary for SVM)
     transformed_text_dense = transformed_text.toarray()  # Convert sparse matrix to dense array
@@ -46,24 +46,19 @@ def predict_sentiment(review_text, model_name):
 
 # Step 6.4: Put all components together in Gradio's interface
 with gr.Blocks() as demo:
-    with gr.Row():
-        # Input components on the left
-        with gr.Column():
-            review_input = gr.Textbox(label="Review Comment", lines=2, placeholder="Enter your review comment here...", elem_id="review-text")
-            model_dropdown = gr.Dropdown(choices=["Logistic Regression", "K-Nearest Neighbors", "Random Forest", "SVM"], label="Select Model", elem_id="model-dropdown")
-            submit_button = gr.Button("Submit", elem_id="submit-btn")
-            clear_button = gr.Button("Clear", elem_id="clear-btn")
-
-        # Output components on the right
-        with gr.Column():
-            output_sentiment = gr.Textbox(label="Predicted Sentiment Class", placeholder="Predicted sentiment will appear here...", elem_id="sentiment-output")
-            output_probability = gr.Textbox(label="Predicted Probability", placeholder="Probabilities will appear here...", elem_id="probability-output")
-
+    # Input components
+    review_input = gr.Textbox(label="Review Comment", lines=2, placeholder="Enter your review comment here...")
+    model_dropdown = gr.Dropdown(choices=["Logistic Regression", "K-Nearest Neighbors", "Random Forest", "SVM"], label="Select Model")
+    
+    # Output components
+    output_sentiment = gr.Textbox(label="Predicted Sentiment Class", placeholder="Predicted sentiment will appear here...")
+    output_probability = gr.Textbox(label="Predicted Probability", placeholder="Probabilities will appear here...")
+    
+    # Submit Button
+    submit_button = gr.Button("Submit")
+    
     # Define the action when the button is clicked
     submit_button.click(fn=predict_sentiment, inputs=[review_input, model_dropdown], outputs=[output_sentiment, output_probability])
 
-    # Reset inputs when clear button is clicked
-    clear_button.click(fn=lambda: ("", "", ""), inputs=[], outputs=[review_input, output_sentiment, output_probability])
-
 # Launch the interface
 demo.launch()