Update app.py

app.py

@@ -22,7 +22,7 @@ def predict_sentiment(custom_data):
             st.error(f"Model file not found: {model_path}")
             return None
         model = load_model(model_path)
-
+        
         # Load the one-hot encoding information
         one_hot_info_path = 'one_hot_info_1.pkl'
         if not os.path.exists(one_hot_info_path):
@@ -50,17 +50,13 @@ def predict_sentiment(custom_data):
         # Predict the sentiments for all tweets
         predictions = model.predict(np.array(padded_texts))
 
-        # Debug: Print predictions
-        st.write("Debug: Predictions")
-        st.write(predictions)
-
         # Convert predictions to class labels and probabilities
         predicted_sentiments = []
         for prediction in predictions:
-            sentiment_probabilities = {label: round(prob, 4) for label, prob in zip(labels_with_emojis.keys(), prediction)}
             sentiment = np.argmax(prediction)
             sentiment_label = list(labels_with_emojis.keys())[sentiment]
             sentiment_emoji = labels_with_emojis[sentiment_label]
+            sentiment_probabilities = {label: round(prob, 4) for label, prob in zip(labels_with_emojis.keys(), prediction)}
             predicted_sentiments.append((sentiment_label, sentiment_emoji, sentiment_probabilities))
 
         return predicted_sentiments
@@ -76,10 +72,10 @@ if st.button('Analyze'):
     if user_input.strip():  # Check if input is not empty
         # Remove emojis and replace with their description
         user_input = emoji.demojize(user_input)
-
+        
         # Split input by newlines to handle multiple tweets
         tweets = user_input.split('\n')
-
+        
         # Predict sentiment for custom data
         predicted_sentiments = predict_sentiment(tweets)