Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import pickle
+import json
+import re
+import tensorflow as tf
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+# --- Load Preprocessing Objects ---
+@st.cache_resource
+def load_resources():
+    model = tf.keras.models.load_model("ensemble_model.keras")
+    with open("tokenizer.pkl", "rb") as f:
+        tokenizer = pickle.load(f)
+    with open("config.json", "r") as f:
+        config = json.load(f)
+    return model, tokenizer, config['max_length']
+
+model, tokenizer, max_length = load_resources()
+
+# --- Text Normalization ---
+NON_ALPHANUM = re.compile(r'\W')
+NON_ASCII = re.compile(r'[^a-z0-1\s]')
+def normalize_text(text):
+    return NON_ASCII.sub('', NON_ALPHANUM.sub(' ', text.lower()))
+
+# --- Inference ---
+def predict_sentiment(text):
+    text = normalize_text(text)
+    sequence = tokenizer.texts_to_sequences([text])
+    padded = pad_sequences(sequence, maxlen=max_length)
+    prediction = model.predict(padded)[0][0]
+    sentiment = "Positive 😊" if prediction > 0.5 else "Negative 😞"
+    return sentiment, float(prediction)
+
+# --- UI ---
+st.title("🗣️ Sentiment Analysis of Customer Reviews")
+st.markdown("This app uses a deep learning model to predict the **sentiment** of customer feedback — whether it's **positive** or **negative**.")
+
+user_input = st.text_area("Enter a customer review:")
+
+if st.button("Analyze"):
+    if user_input.strip() == "":
+        st.warning("Please enter some text to analyze.")
+    else:
+        sentiment, confidence = predict_sentiment(user_input)
+        st.subheader("Prediction")
+        st.write(f"**Sentiment:** {sentiment}")
+        st.write(f"**Confidence:** {confidence:.2%}")