import gradio as gr
import transformers
import numpy as np


from transformers import DistilBertTokenizer, TFAutoModelForSequenceClassification

tokenizer = DistilBertTokenizer.from_pretrained("Gokce/finetuned_distilBERT_forDetecting_GeneratedReviews_TR")
model = TFAutoModelForSequenceClassification.from_pretrained("Gokce/finetuned_distilBERT_forDetecting_GeneratedReviews_TR")


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()