Update app.py

app.py

@@ -1,87 +1,113 @@
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Disable GPU
+
 import gradio as gr
 import numpy as np
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 import pickle
-import os
-
-os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Disable GPU
-
+import re  # Added for text preprocessing
 
+# 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):
+    if not text or not text.strip():  # Check for empty input
+        return 0, [0, 0, 0, 0, 0]  # Return default values for empty input
+    
     cleaned = preprocess_text(text)
+    if not cleaned:  # Check if cleaned text is empty
+        return 0, [0, 0, 0, 0, 0]  # Return default values for invalid input
+    
     seq = tokenizer.texts_to_sequences([cleaned])
+    if not seq or not seq[0]:  # Check if tokenization failed
+        return 0, [0, 0, 0, 0, 0]  # Return default values for invalid input
+    
     padded_seq = pad_sequences(seq, maxlen=200)
     probs = model.predict(padded_seq)
     predicted_class = np.argmax(probs, axis=1)[0]
     rating = predicted_class + 1
     return rating, probs[0]
 
-
+# Main prediction function
 def predict_all_models(text):
-    gru_rating, gru_probs = predict_sentiment(gru_model, text)
-    lstm_rating, lstm_probs = predict_sentiment(lstm_model, text)
-    bilstm_rating, bilstm_probs = predict_sentiment(bilstm_model, text)
-
-    ratings = [gru_rating, lstm_rating, bilstm_rating]
-    lowest = min(ratings)
-    highest = max(ratings)
-    average = sum(ratings) / len(ratings)
-
-    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}"
-    }
-
+    try:
+        # 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}"
+        }
+    except Exception as e:
+        print(f"Error during prediction: {e}")  # Debugging: Print the error
+        results = {
+            "GRU Model": "Error",
+            "LSTM Model": "Error",
+            "BiLSTM Model": "Error",
+            "Statistics": "Error"
+        }
+    
     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 handler for the predict button
         predict_button.click(
             fn=predict_all_models,
             inputs=text_input,
             outputs=[gru_output, lstm_output, bilstm_output, stats_output]
         )
-
+    
     return demo
 
-
+# Launch the app
 if __name__ == "__main__":
     demo = create_interface()
     demo.launch()