Create app.py

app.py

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+import streamlit as st
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.text import tokenizer_from_json
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import json
+import numpy as np
+
+# Load tokenizer
+with open("tokenizer.json", "r") as f:
+    data = json.load(f)
+tokenizer = tokenizer_from_json(json.dumps(data))
+
+# Parameters
+max_tokens = 100  # Set this to the same as used during training
+
+# Load model
+model = load_model("review_amazon_sentiment5.h5")
+
+# Streamlit UI
+st.title("Amazon Review Sentiment Analyzer")
+user_input = st.text_area("Enter an Amazon product review:")
+
+if st.button("Analyze"):
+    if user_input.strip():
+        tokens = tokenizer.texts_to_sequences([user_input])
+        tokens_padded = pad_sequences(tokens, maxlen=max_tokens)
+        pred_prob = model.predict(tokens_padded)[0][0]
+        sentiment = "🟢 Positive" if pred_prob < 0.5 else "🔴 Negative"
+        st.markdown(f"**Sentiment:** {sentiment}")
+        st.markdown(f"**Confidence:** {pred_prob:.2f}")
+    else:
+        st.warning("Please enter some text to analyze.")