fn9

app.py

@@ -1,3 +1,35 @@
 import gradio as gr
+import tensorflow as tf
+import numpy as np
 
-gr.Interface.load("sentimentality.h5").launch()
+# Load the pre-trained model
+model = tf.keras.models.load_model('sentimentality.h5')
+
+# Define a function to preprocess the text input
+def preprocess(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')
+    return text
+
+# Define a function to make a prediction on the input text
+def predict_sentiment(text):
+    # Preprocess the text
+    text = preprocess(text)
+    # 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"), 
+    outputs=gr.outputs.Label(label="Sentiment", default="Neutral")
+)
+
+# Launch the interface
+iface.launch()