Update app.py

app.py

@@ -3,20 +3,24 @@ import gradio as gr
 import tensorflow as tf
 from tensorflow.keras.layers import TextVectorization
 
-def sentiment_pred(text):
-    load_vectorizer = pickle.load(open("./tv_layer.pkl", "rb"))
+def sentiment_pred2(text):
+    load_vectorizer = pickle.load(open("tv_layer.pkl", "rb"))
     new_vectorizer = TextVectorization.from_config(load_vectorizer['config'])
 # You have to call `adapt` with some dummy data (BUG in Keras)
     new_vectorizer.adapt(tf.data.Dataset.from_tensor_slices(["xyz"])) #some argues that it is not necessary 
     new_vectorizer.set_weights(load_vectorizer['weights'])
 
     vectorized_text = new_vectorizer([text])
-    load_model = tf.keras.models.load_model('./ar_sentiment_2.h5')
+    load_model = tf.keras.models.load_model('ar_sentiment_15_ep.h5')
     result = load_model.predict([vectorized_text])
-    x = np.argmax(result[0])+1
-    return x 
+    pred = np.argmax(result[0])+1
+    labels = ["نص يعبر عن الاستياء","نص يعبر عن الحياد","نص يعبر عن الثناء"]
+    print(pred)
+    print(np.argmax(pred))
+    print(labels[np.argmax(pred)-1])
+    return (labels[np.argmax(pred)-1]) 
     
-    interface = gr.Interface(fn=sentiment_pred, 
+    interface = gr.Interface(fn=sentiment_pred2, 
                          inputs=gr.inputs.Textbox(lines=2, placeholder='enter your text'),
                          outputs=["text"])
     interface.launch()