import tensorflow as tf
import gradio as gr
from pathlib import Path

# model_v1 = tf.keras.models.load_model("models/v1.hdf5")
# model_v2 = tf.keras.models.load_model("models/v2.hdf5")
model_v3 = tf.keras.models.load_model("models/v3.hdf5")
class_names=['Real', 'Fake']

def predict(image) -> str:
  image = image.resize((224, 224))
  img = tf.keras.preprocessing.image.img_to_array(image)
  img = tf.expand_dims(img, axis=0)

  # y_pred_v1 = tf.round(model_v1.predict(img, verbose=0)[0])
  # y_pred_v2 = tf.round(model_v2.predict(img, verbose=0)[0])
  y_pred_v3 = tf.round(model_v3.predict(img, verbose=0)[0])

  # return class_names[int(y_pred_v1)], class_names[int(y_pred_v2)], class_names[int(y_pred_v3)]
  return class_names[int(y_pred_v3)]

TITLE = "Anti Spoof Detection (MUKHAM)"

interface = gr.Interface(predict, 
                         inputs=gr.Image(source='upload', type='pil'),
                         # outputs=[gr.Textbox(label="V1 Prediction [Acc: 0.970 | Loss: 0.100]"),
                         #          gr.Textbox(label="V2 Prediction [Acc: 0.985 | Loss: 0.039]"),
                         #          gr.Textbox(label="V3 Prediction [Acc: 0.997 | Loss: 0.005]")],
                         outputs=[gr.Textbox(label="Model Prediction [Acc: 0.997 | Loss: 0.005]")],
                         title=TITLE)
                         # examples=[[i] for i in Path('/examples')])

interface.launch(debug=True)