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import streamlit as st
import numpy as np
from keras.models import load_model
from PIL import Image
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Load the GRU model
model = load_model('model_gru_2')
def run():
image = Image.open('twittersentiment.jpg')
st.image(image, caption = 'Twitter Sentiment')
with st.form('sentiment_prediction'):
# Field Input Text
input_text = st.text_area('Input Text', '', help='Enter the text for sentiment prediction')
# Create a submit button
submitted = st.form_submit_button('Predict')
# Inference
if submitted:
# Make a prediction using the model
# Convert the input text to lowercase (optional)
input_text = input_text.lower()
# Make a prediction using the model
predictions = model.predict(np.array([input_text]))
# Map predicted class to labels
predicted_class = np.argmax(predictions[0])
class_labels = {0: 'Negative', 1: 'Positive', 2: 'Neutral'}
predicted_label = class_labels[predicted_class]
# Display the results
st.write('## Sentiment Prediction:')
st.write('Input Text:', input_text)
st.write('Predicted Class:', predicted_class)
st.write('Predicted Label:', predicted_label)
st.write('Prediction Probabilities:', predictions[0])
if __name__ == '__main__':
run()
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