Create app.py

app.py

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+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+import gradio as gr
+
+tokenizer = tf.keras.preprocessing.text.Tokenizer()
+
+#Reads Text Inputs Here
+f=open('Inputs.txt','r')
+inputs = f.read().split('\n')
+f.close()
+
+corpus = inputs
+
+tokenizer.fit_on_texts(corpus)
+sequences = tokenizer.texts_to_sequences(corpus)
+
+max_length = max([len(s) for s in sequences])
+
+# Load your saved model
+model = tf.keras.models.load_model('sentiment_mini-test')
+
+def use(input_text):
+  # Preprocess the input text
+  sequences = tokenizer.texts_to_sequences([input_text])
+  sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, padding='post', maxlen=max_length)
+
+  # Make a prediction on the input text
+  prediction = model.predict(sequences)[0]
+
+  # Print the prediction
+  return round(prediction[0])
+
+
+iface = gr.Interface(fn=use, inputs="text", outputs="text")
+iface.launch()