import tensorflow as tf
from tensorflow.keras.models import load_model
import gradio as gr
import numpy as np
import cv2
import numpy as np
import os
import sys
from tensorflow import keras
from tensorflow.keras import layers


def get_model():
    model = load_model("model_1.h5")
    model.compile(optimizer="adam", loss="categorical_crossentropy")
    model.summary()
    return model


model = get_model()

labels = ["zero","one","two","three","four","five","six","seven","eight","nine","ten","eleven","twelve","thrteen","fourteen","fifteen","sixteen","seventeen","eightteen","nineteen"]

def predict(img):
    img = cv2.resize(img, (64, 64), interpolation=cv2.INTER_AREA)
    img = img.astype("float32")
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img = img.reshape(1, 64, 64, 1)
    print(img.shape)
    out = model.predict(img)
    print(out.shape)
    cls = out.argmax()
    return cls

ui = gr.Interface(
fn=predict,
inputs=gr.Image(type="numpy"),
outputs=gr.Textbox()
)

ui.launch()