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app.py

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+import streamlit as st
+import numpy as np
+import pickle
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import Tokenizer
+
+# Load the model, tokenizer, and label map
+model = load_model('sentiment_model.h5')
+
+with open('tokenizer.pkl', 'rb') as file:
+    tokenizer = pickle.load(file)
+
+with open('label_map.pkl', 'rb') as file:
+    label_map = pickle.load(file)
+
+# Define max length
+max_len = 100
+
+def preprocess_text(text, tokenizer, max_len):
+    sequence = tokenizer.texts_to_sequences([text])
+    padded_sequence = pad_sequences(sequence, maxlen=max_len)
+    return padded_sequence
+
+def predict_sentiment(text, model, tokenizer, max_len, label_map):
+    processed_text = preprocess_text(text, tokenizer, max_len)
+    prediction = model.predict(processed_text)
+    predicted_class = np.argmax(prediction, axis=1)[0]
+    predicted_label = label_map[predicted_class]
+    return predicted_label
+
+# Streamlit app
+st.title("Sentiment Analysis App")
+
+st.write("Enter a text to predict its sentiment:")
+
+text_input = st.text_area("Text", "Type your text here...")
+
+if st.button('Predict'):
+    if text_input:
+        predicted_sentiment = predict_sentiment(text_input, model, tokenizer, max_len, label_map)
+        st.write(f"The predicted sentiment for the text is: {predicted_sentiment}")
+    else:
+        st.write("Please enter some text.")

label_map.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:830cdc1ebbe08ac7e30453e6d4f046230fdf391fef1703e537318f06fcf2e749
+size 69

sentiment_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:090f966b300c5d6ec9f3f1e58a955af76c0d8b3d8bdfb0824f667dfae5e2cd29
+size 11063200

tokenizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ecec734451f1bcb40e50aa54b8f4503b2007b546cb32a41f91dd2458c8e4dec8
+size 1236455