import gradio as gr
import tensorflow as tf
from PIL import Image
import numpy as np

model = tf.keras.models.load_model('food_or_not_v2.h5')

def prob(x):
    probs = {
        "Food": 0.5,
        "Not Food": 0.5
    }
    if(x<0.5):
        probs["Food"] = (1-(x*2))
        probs["Not Food"] = (x*2)
    else:
        probs["Food"] = 1-(1-((1-x)*2))
        probs["Not Food"] = (1-((1-x)*2))
    return probs

def get_pred(img):
    try:
        img = Image.fromarray(img.astype('uint8'), 'RGB')
        img = img.resize((224, 224))
        img = np.array(img)
        img = np.expand_dims(img, axis=0)
        val = model.predict(img)
        val = val[0][0]
        return prob(val)
    except Exception as e:
        return "Error"

iface = gr.Interface(
    fn=get_pred,
    inputs='image',
    outputs='label',
    title="Food or Not Food Classifier",
    description="Upload an image to check if it contains food or not.",
    examples=["example1.jpg", "example2.png"]  # Provide paths to example images
)

iface.launch()