Update app.py

app.py

@@ -8,22 +8,22 @@ from tensorflow.keras.preprocessing.sequence import pad_sequences
 import pickle
 import re
 
-# Load your models
+# Load 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
+# Load tokenizer
 with open("my_tokenizer.pkl", "rb") as f:
     tokenizer = pickle.load(f)
 
-# Preprocessing function for GRU, LSTM, and BiLSTM
+
 def preprocess_text(text):
     text = text.lower()
     text = re.sub(r"[^a-zA-Z\s]", "", text).strip()
     return text
 
-# GRU prediction function
+
 def predict_with_gru(text):
     cleaned = preprocess_text(text)
     seq = tokenizer.texts_to_sequences([cleaned])
@@ -32,7 +32,7 @@ def predict_with_gru(text):
     predicted_class = np.argmax(probs, axis=1)[0]
     return int(predicted_class + 1)
 
-# LSTM prediction function
+
 def predict_with_lstm(text):
     cleaned = preprocess_text(text)
     seq = tokenizer.texts_to_sequences([cleaned])
@@ -41,7 +41,7 @@ def predict_with_lstm(text):
     predicted_class = np.argmax(probs, axis=1)[0]
     return int(predicted_class + 1)
 
-# BiLSTM prediction function
+
 def predict_with_bilstm(text):
     cleaned = preprocess_text(text)
     seq = tokenizer.texts_to_sequences([cleaned])
@@ -94,8 +94,11 @@ with gr.Blocks(css=".gradio-container { max-width: 900px; margin: auto; padding:
                 placeholder="Type your review here..."
             )
         
+        analyze_button = gr.Button("Analyze Sentiment", variant="primary")
+        analyze_button.style(full_width=False) 
+
         with gr.Column():
-            analyze_button = gr.Button("Analyze Sentiment")
+            statistics_output = gr.Textbox(label="Statistics (Lowest, Highest, Average)", interactive=False) 
 
     with gr.Row():
         with gr.Column():
@@ -135,5 +138,5 @@ with gr.Blocks(css=".gradio-container { max-width: 900px; margin: auto; padding:
         ]
     )
 
-# Launch the app
+
 demo.launch()