Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+import pickle
+import numpy as np
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+# Load the model and tokenizer
+model = tf.keras.models.load_model('sentiment_model.keras')
+
+with open('tokenizer.pickle', 'rb') as handle:
+    tokenizer = pickle.load(handle)
+
+with open('max_length.txt', 'r') as f:
+    max_length = int(f.read())
+
+def classify_sentence(sentence):
+    seq = tokenizer.texts_to_sequences([sentence])
+    padded_seq = pad_sequences(seq, maxlen=max_length)
+    prediction = model.predict(padded_seq)
+    label = "Positive" if prediction[0][0] > 0.5 else "Negative"
+    return label
+
+# Streamlit UI
+st.title("Restaurant Review Sentiment Analysis")
+st.write("Enter your review in Turkish to analyze its sentiment")
+
+user_input = st.text_area("Enter your review:")
+
+if st.button("Analyze"):
+    if user_input:
+        result = classify_sentence(user_input)
+        st.write(f"Sentiment: {result}")
+    else:
+        st.write("Please enter a review to analyze")