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]}')