app.py

import streamlit as st
from PIL import Image
import jax
import numpy as np
import jax.numpy as jnp  # JAX NumPy
from flax.training import train_state  # Useful dataclass to keep train state
from flax import linen as nn  # Linen API
from huggingface_hub import HfFileSystem
from flax.serialization import msgpack_restore, from_state_dict
import os
import tensorflow as tf

hf_key =  text_input = st.text_input("Access token")

class CNN(nn.Module):
  @nn.compact
  def __call__(self, x):
    x = nn.Conv(features=32, kernel_size=(3, 3))(x)
    x = nn.relu(x)
    x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2))
    x = nn.Conv(features=64, kernel_size=(3, 3))(x)
    x = nn.relu(x)
    x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2))
    x = x.reshape((x.shape[0], -1)) # flatten
    x = nn.Dense(features=256)(x)
    x = nn.relu(x)
    x = nn.Dense(features=16)(x)
    x = nn.relu(x)
    x = nn.Dense(features=2)(x)
    return x

cnn = CNN()
params = cnn.init(jax.random.PRNGKey(0), jnp.ones([2, 50, 50, 3]))['params']

fs = HfFileSystem(token=hf_key)
with fs.open("PrakhAI/CatVsDog/checkpoint.msgpack", "rb") as f:
  params = from_state_dict(params, msgpack_restore(f.read())["params"])

uploaded_files = st.file_uploader("Input images of cats or dogs (examples in files)", type=['jpg','png','tif'], accept_multiple_files=True)

if len(uploaded_files) == 0:
  st.write("Please upload an image!")
else:
  for uploaded_file in uploaded_files:
    img = Image.open(uploaded_file)
    st.image(img)
    input = jnp.array(tf.cast(tf.image.resize(tf.convert_to_tensor(img), [50, 50]), tf.float32) / 255.)
    st.write("Input shape: " + input.shape)
    st.write("Model Prediction: " + cnn.apply({"params": params}, input))
    st.write("Model Prediction type: " + type(cnn.apply({"params": params}, input)))
    st.write("Model Prediction type dir: " + dir(cnn.apply({"params": params}, input)))

def gridify(kernel, grid, kernel_size, scaling=5, padding=1):
  scaled_and_padded = np.pad(np.repeat(np.repeat(kernel, repeats=scaling, axis=0), repeats=scaling, axis=1), ((padding,),(padding,),(0,),(0,)), 'constant', constant_values=(-1,))
  grid = np.pad(np.array(scaled_and_padded.reshape((kernel_size[0]*scaling+2*padding, kernel_size[1]*scaling+2*padding, 3, grid[0], grid[1])).transpose(3,0,4,1,2).reshape(grid[0]*(kernel_size[0]*scaling+2*padding), grid[1]*(kernel_size[1]*scaling+2*padding), 3)+1)*127., ((padding,),(padding,),(0,)), 'constant', constant_values=(0,))
  st.image(Image.fromarray(grid.astype(np.uint8), mode="RGB"))

with st.expander("See first convolutional layer"):
  gridify(params["Conv_0"]["kernel"], grid=(4,8), kernel_size=(3,3))
    
with st.expander("See second convolutional layer"):
  print(params["Conv_1"]["kernel"].shape)
  gridify(params["Conv_1"]["kernel"], grid=(64,96), kernel_size=(3,3))