Update app.py

app.py

@@ -4,24 +4,29 @@ import tensorflow as tf
 import joblib
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 
-# Load model and tokenizer
+# Load the trained model
 model = tf.keras.models.load_model("sentiment_model.h5")
-tokenizer = joblib.load("tokenizer.joblib")
 
+# Load the tokenizer
+tokenizer = joblib.load("tokenizer.joblib")
 max_len = 40
 
+# Inference function
 def predict_sentiment(text):
     seq = tokenizer.texts_to_sequences([text])
     padded = pad_sequences(seq, maxlen=max_len, padding='post')
     pred = model.predict(padded)[0][0]
     label = "Positive" if pred >= 0.5 else "Negative"
-    return {label: float(pred) if label == "Positive" else 1 - float(pred)}
+    confidence = float(pred) if label == "Positive" else 1 - float(pred)
+    return {label: round(confidence, 4)}
 
 # Gradio UI
-demo = gr.Interface(fn=predict_sentiment,
-                    inputs=gr.Textbox(lines=2, placeholder="Enter a tweet..."),
-                    outputs=gr.Label(num_top_classes=2),
-                    title="Sentiment Analysis on Tweets",
-                    description="Enter a tweet and get predicted sentiment (Positive/Negative) and confidence score.")
+demo = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a tweet here..."),
+    outputs=gr.Label(num_top_classes=2),
+    title="Tweet Sentiment Analyzer (BiLSTM + Attention)",
+    description="This model was trained from scratch using LSTM + Attention to predict sentiment of tweets (Positive/Negative)."
+)
 
 demo.launch()