Update app.py

app.py

@@ -3,31 +3,95 @@ import numpy as np
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 import pickle
-import re
 
-# Load model and tokenizer
-model = load_model("best_GRU_tuning_model.h5")
-with open("my_tokenizer.pkl","rb") as f:
-    tokenizer = pickle.load(f)
+# 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
 
-
-def predict_sentiment(raw_text):
-    cleaned = preprocess_text(raw_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
-    return f"Predicted rating: {rating} (probabilities={probs[0]})"
+    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
 
-demo = gr.Interface(fn=predict_sentiment,
-                    inputs="text",
-                    outputs="label")
-demo.launch()
+# Launch the app
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch()