Create app.py

app.py

@@ -0,0 +1,36 @@
+import gradio as gr
+from transformers import pipeline
+
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("Gokce/finetuned-distilbert-multi-rr")
+model = AutoModelForSequenceClassification.from_pretrained("Gokce/finetuned-distilbert-multi-rr")
+
+
+MAX_LENGTH = 128
+
+def predict(text):
+    inputs = tokenizer.encode_plus(text, add_special_tokens=True, return_tensors="tf", padding=True, truncation=True, max_length=MAX_LENGTH)
+
+    predictions = model.predict([inputs['input_ids'], inputs['attention_mask']])
+    predicted_class_idx = np.argmax(predictions[0])
+
+    if predicted_class_idx == 0:
+        predicted_class = "Human"
+    else:
+        predicted_class = "AI"
+
+    return predicted_class
+
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Textbox(lines=5, placeholder="Enter text...", label='Review Text'),
+    outputs=gr.outputs.Textbox(label='Detected Label'),
+    title='Generated Text Classifier for Restaurant Reviews'
+)
+iface.launch()
+
+
+