direct uthaya

app.py

@@ -1,33 +1,3 @@
 import gradio as gr
-import tensorflow as tf
 
-# Load the pre-trained model
-model = tf.keras.models.load_model('sentimentality.h5')
-
-# Define a function to make a prediction on the input text
-def predict_sentiment(text):
-    # Preprocess the text
-    tokenizer = tf.keras.preprocessing.text.Tokenizer()
-    tokenizer.fit_on_texts([text])
-    text = tokenizer.texts_to_sequences([text])
-    text = tf.keras.preprocessing.sequence.pad_sequences(text, maxlen=500, padding='post', truncating='post')
-    # Make a prediction using the loaded model
-    proba = model.predict(text)[0]
-    # Normalize the probabilities
-    proba /= proba.sum()
-    # Return the probability distribution
-    return {"Positive": float(proba[0]), "Negative": float(proba[1]), "Neutral": float(proba[2])}
-
-# Create a Gradio interface
-iface = gr.Interface(
-    fn=predict_sentiment,
-    inputs=gr.inputs.Textbox(label="Enter text here", lines=5, placeholder="Type here to analyze sentiment..."),
-    outputs=gr.outputs.Label(label="Sentiment", default="Neutral", font_size=30)
-)
-
-# Add the possible classes to the output plot
-classes = ["Positive", "Negative", "Neutral"]
-iface.outputs[0].choices = classes
-
-# Launch the interface
-iface.launch()
+gr.Interface.load("sentimentality.h5").launch()