Update app.py

app.py

@@ -1,19 +1,29 @@
-from tensorflow.keras.models import model_from_json
 import tensorflow as tf
+from tensorflow.keras.layers import Layer, Dense
+import gradio as gr
 import joblib
 from tensorflow.keras.preprocessing.sequence import pad_sequences
-import gradio as gr
 
-# Load model architecture
-with open("model_architecture.json", "r") as f:
-    model = model_from_json(f.read())
+# 🔸 Define Custom Layer Again
+class BetterAttention(Layer):
+    def __init__(self, units=64, return_attention=False, **kwargs):
+        super(BetterAttention, self).__init__(**kwargs)
+        self.return_attention = return_attention
+        self.W = Dense(units)
+        self.V = Dense(1)
 
-# Load weights
-model.load_weights("model_weights.weights.h5")
+    def call(self, inputs):
+        score = self.V(tf.nn.tanh(self.W(inputs)))
+        attention_weights = tf.nn.softmax(score, axis=1)
+        context_vector = attention_weights * inputs
+        context_vector = tf.reduce_sum(context_vector, axis=1)
+        return (context_vector, attention_weights) if self.return_attention else context_vector
 
-# Load tokenizer
+# 🔸 Load model & tokenizer
+model = tf.keras.models.load_model("model.keras", custom_objects={"BetterAttention": BetterAttention})
 tokenizer = joblib.load("tokenizer.joblib")
 
+# 🔸 Define prediction
 max_len = 40
 
 def predict_sentiment(text):
@@ -21,14 +31,14 @@ def predict_sentiment(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 pred >= 0.5 else 1 - pred)
+    return {label: confidence}
 
-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."
-)
+# 🔸 Gradio Interface
+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 with confidence score.")
 
 demo.launch()