Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import tensorflow as tf
+import text_hammer as th
+from transformers import BertTokenizer, BertModel
+
+name = "MiVaCod/mbart-neutralization"
+tokenizer = BertTokenizer.from_pretrained(name)
+model = BertModel.from_pretrained(name)
+
+# Define a function to make predictions
+def predict(texts):
+    # Tokenize and preprocess the new text
+    new_encodings = tokenizer(texts, truncation=True, padding=True, max_length=70, return_tensors='tf')
+    new_predictions = model(new_encodings)
+    # Make predictions
+    new_predictions = model(new_encodings)
+    new_labels_pred = tf.argmax(new_predictions.logits, axis=1)
+    new_labels_pred = new_labels_pred.numpy()[0]
+
+    labels_list = ["Negative 😠", "Positive 😍"]
+    emotion = labels_list[new_labels_pred]
+    return emotion
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs="text",
+    outputs=gr.outputs.Label(num_top_classes = 6),  # Corrected output type
+    examples=[["the rock is destined to be the 21st century's new conan and that he's going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal."],
+              ],
+    title="Rotten tomatoes classification",
+    description="Predict the class associated with a text."
+)
+# Launch the interfac
+iface.launch()