Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,75 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import tensorflow as tf
+import pickle
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import numpy as np
+
+# --- 1. CONFIGURATION & SETUP ---
+st.set_page_config(page_title="IMDb Sentiment Analysis", page_icon="🎬")
+
+# Constants (Must match training parameters)
+MAX_LEN = 200
+MODEL_PATH = "src/sentiment_lstm_model.h5"
+TOKENIZER_PATH = "src/tokenizer.pickle"
+
+# --- 2. LOAD RESOURCES (Cached for performance) ---
+@st.cache_resource
+def load_model():
+    model = tf.keras.models.load_model(MODEL_PATH)
+    return model
+
+@st.cache_resource
+def load_tokenizer():
+    with open(TOKENIZER_PATH, 'rb') as handle:
+        tokenizer = pickle.load(handle)
+    return tokenizer
+
+try:
+    model = load_model()
+    tokenizer = load_tokenizer()
+    st.success("System loaded successfully! 🚀")
+except Exception as e:
+    st.error(f"Error loading files: {e}")
+    st.stop()
+
+# --- 3. PREDICTION FUNCTION ---
+def predict_sentiment(review_text):
+    # Preprocess the text
+    # Note: Text cleaning is handled implicitly by the tokenizer to some extent,
+    # but for best results, basic lowercase/strip is recommended.
+    review_seq = tokenizer.texts_to_sequences([review_text])
+    review_pad = pad_sequences(review_seq, maxlen=MAX_LEN)
+    
+    # Predict
+    probability = model.predict(review_pad)[0][0]
+    
+    if probability > 0.5:
+        return "Positive", probability
+    else:
+        return "Negative", 1 - probability
+
+# --- 4. STREAMLIT UI ---
+st.title("🎬 Movie Review Sentiment Analysis")
+st.markdown("Enter a movie review below to check if it's **Positive** or **Negative**.")
+st.markdown("*(Powered by LSTM & Word2Vec)*")
+
+# Text Input
+user_input = st.text_area("Write your review here (English):", height=150)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if st.button("Analyze Sentiment"):
+    if user_input.strip() == "":
+        st.warning("Please enter some text first.")
+    else:
+        with st.spinner("Analyzing..."):
+            sentiment, score = predict_sentiment(user_input)
+            
+        # Display Result
+        st.divider()
+        if sentiment == "Positive":
+            st.balloons()
+            st.success(f"**Verdict:** {sentiment} 😊")
+        else:
+            st.error(f"**Verdict:** {sentiment} 😞")
+            
+        st.progress(float(score))
+        st.caption(f"Confidence Score: {score:.2%}")