Create app.py

app.py

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+import streamlit as st
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import pickle 
+
+# Load the LSTM model
+lstm_model = load_model('lstm.h5')
+
+# Load the Tokenizer used during training
+with open('tokenizer.pkl', 'rb') as tokenizer_file:
+    tokenizer = pickle.load(tokenizer_file)
+
+# Define class labels and their numerical mapping
+class_mapping = {"Angry": 0, "Sad": 1, "Joy": 2, "Surprise": 3}
+numerical_to_label = {v: k for k, v in class_mapping.items()}
+
+st.title('Model Deployment')
+
+# Text input for the user to enter a sequence
+user_input = st.text_input('Enter a sequence:')
+
+if st.button('Predict'):
+    # Tokenize and pad the user input
+    sequence = tokenizer.texts_to_sequences([user_input])
+    padded_sequence = pad_sequences(sequence, maxlen=128)
+
+    # Make predictions
+    prediction = lstm_model.predict(padded_sequence)
+
+    # Display the predicted probabilities and labels
+    st.write('Predicted Probabilities:')
+    for i in range(len(prediction[0])):
+        label = numerical_to_label[i]
+        st.write(f'{label}: {prediction[0][i]}')