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

# Load your model here
model = tf.keras.models.load_model('food_or_not.keras')

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):
    img = img.resize((224, 224))
    img = np.array(img)
    img = np.expand_dims(img, axis=0)
    val = model.predict(img)
    val = val[0][0]
    # print(val)
    # if val < 0.5:
    #     return "Food"
    # else:
    #     return "Not Food"
    return prob(val)

# iface = gr.Interface(
#     fn=get_pred,
#     inputs=gr.inputs.Image(type="pil", label="Upload an Image"),
#     outputs=gr.outputs.Textbox(label="Prediction"),
#     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 = gr.Interface(
    fn=get_pred,
    inputs=gr.inputs.Image(type="pil", label="Upload an Image"),
    outputs=gr.outputs.Label(num_top_classes=2, label="Prediction"),
    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()