import gradio as gr
import requests
import torch
import torch.nn as nn

import timm

model = timm.create_model("hf_hub:nateraw/resnet18-random", pretrained=True)
model.eval()

import os 

def print_bn():
    bn_data = []
    for m in model.modules():
        if(type(m) is nn.BatchNorm2d):
            # print(m.momentum)
            bn_data.extend(m.running_mean.data.numpy().tolist())
            bn_data.extend(m.running_var.data.numpy().tolist())
            bn_data.append(m.momentum)
    return bn_data

def update_bn(image):
    cursor_im = 0
    image = image.view(-1)
    for m in model.modules():
        if(type(m) is nn.BatchNorm2d):
            if(cursor_im < image.shape[0]):
                M = m.running_mean.data.shape[0]
                if(cursor_im+M < image.shape[0]):
                    m.running_mean.data = image[cursor_im:cursor_im+M]
                    cursor_im += M # next
                else:
                    m.running_mean.data[:image.shape[0]-cursor_im] = image[cursor_im:]
                    break # finish 
    return
    

def greet(image):
    # url = f'https://huggingface.co/spaces?p=1&sort=modified&search=GPT'
    # html = request_url(url)
    # key = os.getenv("OPENAI_API_KEY")
#     x = torch.ones([1,3,224,224])
    if(image is None):
        bn_data = print_bn()
        return ','.join([f'{x:.10f}' for x in bn_data])
    else:  
        print(type(image))
        image = torch.tensor(image).float()
        print(image.min(), image.max())
        image = image/255.0
        image = image.unsqueeze(0)
        print(image.shape)
        image = torch.permute(image, [0,3,1,2])
        out = model(image)
        update_bn(image)
    # model.train()
    return "Hello world!"



image = gr.inputs.Image(label="Upload a photo for beautify", shape=(224,224))
iface = gr.Interface(fn=greet, inputs=image, outputs="text")
iface.launch()