Update app.py

app.py

@@ -4,94 +4,81 @@ from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 import pickle
 
-# Load your models
+
 gru_model = load_model("best_GRU_tuning_model.h5")
 lstm_model = load_model("lstm_model.h5")
 bilstm_model = load_model("bilstm_model.h5")
 
-# Load your tokenizer
+
 with open("my_tokenizer.pkl", "rb") as f:
     tokenizer = pickle.load(f)
 
-# Preprocess text
+
 def preprocess_text(text):
     text = text.lower()
     text = re.sub(r'[^a-zA-Z\s]', '', text).strip()
     return text
 
-# Predict sentiment using a model
+
 def predict_sentiment(model, text):
     cleaned = preprocess_text(text)
     seq = tokenizer.texts_to_sequences([cleaned])
     padded_seq = pad_sequences(seq, maxlen=200)
     probs = model.predict(padded_seq)
     predicted_class = np.argmax(probs, axis=1)[0]
-    rating = predicted_class + 1  
+    rating = predicted_class + 1
     return rating, probs[0]
 
-# Main prediction function
+
 def predict_all_models(text):
-    # Predict with GRU
     gru_rating, gru_probs = predict_sentiment(gru_model, text)
-    # Predict with LSTM
     lstm_rating, lstm_probs = predict_sentiment(lstm_model, text)
-    # Predict with BiLSTM
     bilstm_rating, bilstm_probs = predict_sentiment(bilstm_model, text)
-    
-    # Calculate statistics
+
     ratings = [gru_rating, lstm_rating, bilstm_rating]
     lowest = min(ratings)
     highest = max(ratings)
     average = sum(ratings) / len(ratings)
-    
-    # Format results
+
     results = {
         "GRU Model": f"Predicted Rating: {gru_rating} (Probabilities: {gru_probs})",
         "LSTM Model": f"Predicted Rating: {lstm_rating} (Probabilities: {lstm_probs})",
         "BiLSTM Model": f"Predicted Rating: {bilstm_rating} (Probabilities: {bilstm_probs})",
         "Statistics": f"Lowest: {lowest}, Highest: {highest}, Average: {average:.2f}"
     }
-    
+
     return results
 
-# Gradio interface
+
 def create_interface():
     with gr.Blocks() as demo:
         gr.Markdown("# Sentiment Analysis App")
         gr.Markdown("Predict the sentiment of your text review using RNN-based models.")
-        
+
         with gr.Row():
             text_input = gr.Textbox(label="Enter your text here:", placeholder="Type your review here...")
-        
+
         with gr.Row():
             gr.Markdown("### Predicted Sentiment")
             gru_output = gr.Textbox(label="GRU Model")
             lstm_output = gr.Textbox(label="LSTM Model")
             bilstm_output = gr.Textbox(label="BiLSTM Model")
-        
+
         with gr.Row():
             gr.Markdown("### Statistics")
             stats_output = gr.Textbox(label="Lowest, Highest, Average")
-        
-        # Button to predict
+
         predict_button = gr.Button("Predict Sentiment")
-        
-        # Event handlers
+
         predict_button.click(
             fn=predict_all_models,
             inputs=text_input,
             outputs=[gru_output, lstm_output, bilstm_output, stats_output]
         )
-        
-        sample_review.change(
-            fn=lambda x: x,
-            inputs=sample_review,
-            outputs=text_input
-        )
-    
+
     return demo
 
-# Launch the app
+
 if __name__ == "__main__":
     demo = create_interface()
     demo.launch()