import gradio as gr
from huggingface_hub import from_pretrained_keras
import tensorflow as tf
import numpy as np
from PIL import Image
import io
import base64


model = tf.keras.load_model("./tf_model.h5")



def greet(name):
    return "Hello " + name + "!!"


def predict(image):
    img = np.array(image)

    im = tf.image.resize(img, (128, 128))
    im = tf.cast(im, tf.float32) / 255.0
    pred_mask = model.predict(im[tf.newaxis, ...])

    # take the best performing class for each pixel
    # the output of argmax looks like this [[1, 2, 0], ...]
    pred_mask_arg = tf.argmax(pred_mask, axis=-1)

    labels = []
    
    # convert the prediction mask into binary masks for each class
    binary_masks = {}
    mask_codes = {}
    
    # when we take tf.argmax() over pred_mask, it becomes a tensor object
    # the shape becomes TensorShape object, looking like this TensorShape([128]) 
    # we need to take get shape, convert to list and take the best one
    
    rows = pred_mask_arg[0][1].get_shape().as_list()[0]
    cols = pred_mask_arg[0][2].get_shape().as_list()[0]
    
    for cls in range(pred_mask.shape[-1]):

        binary_masks[f"mask_{cls}"] = np.zeros(shape = (pred_mask.shape[1], pred_mask.shape[2])) #create masks for each class
        
        for row in range(rows):

            for col in range(cols):

                if pred_mask_arg[0][row][col] == cls:
                    
                    binary_masks[f"mask_{cls}"][row][col] = 1
                else:
                    binary_masks[f"mask_{cls}"][row][col] = 0

        mask = binary_masks[f"mask_{cls}"]
        mask *= 255
        img = Image.fromarray(mask.astype(np.int8), mode="L")
            
        # we need to make it readable for the widget
        with io.BytesIO() as out:
            img.save(out, format="PNG")
            png_string = out.getvalue()
            mask = base64.b64encode(png_string).decode("utf-8")

        mask_codes[f"mask_{cls}"] = mask

        # widget needs the below format, for each class we return label and mask string
        labels.append({
            "label": f"LABEL_{cls}",
            "mask": mask_codes[f"mask_{cls}"],
            "score": 1.0,
        })

    return labels["mask"], labels["label"]
    


inputs = gr.inputs.Image(label="Upload a fetal standard plane image", type = 'pil', optional=False)
outputs = [
    gr.outputs.Image(label="Segmentation"),
    gr.outputs.Textbox(type="auto",label="Fetal Plane Prediction")
]

examples = [
    "./examples/cat_1.jpg",
    "./examples/cat_2.jpg",
    "./examples/dog_1.jpg",
    "./examples/dog_2.jpg",
]

iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()

interface = gr.Interface(fn=model.predict, 
    inputs=inputs,
    outputs=outputs,
    # title = title, 
    # description=description,
    examples=examples
    )
interface.launch()