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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
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()
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:
input = jnp.array([tf.cast(tf.image.resize(tf.convert_to_tensor(Image.open(uploaded_file)), [50, 50]), tf.float32) / 255. for uploaded_file in uploaded_files])
predictions = cnn.apply({"params": params}, input)
for (image, prediction) in zip(uploaded_files, predictions):
st.image(Image.open(image))
[cat_prob, dog_prob] = jax.nn.softmax(prediction)
if cat_prob > dog_prob:
st.write(f"Model Prediction - Cat ({100*cat_prob:.2f}%), Dog ({100*dog_prob:.2f}%)")
else:
st.write(f"Model Prediction - Dog ({100*dog_prob:.2f}%), Cat ({100*cat_prob:.2f}%)")
def gridify_rgb(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"))
def gridify_grayscale(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, grid[0], grid[1])).transpose(2,0,3,1).reshape(grid[0]*(kernel_size[0]*scaling+2*padding), grid[1]*(kernel_size[1]*scaling+2*padding))+1)*127., (padding,), 'constant', constant_values=(0,))
st.image(Image.fromarray(np.repeat(np.expand_dims(grid, axis=0), repeats=3, axis=0).astype(np.uint8).transpose(1,2,0), mode="RGB"))
with st.expander("See first convolutional layer"):
gridify_rgb(params["Conv_0"]["kernel"], grid=(4,8), kernel_size=(3,3))
with st.expander("See second convolutional layer"):
gridify_grayscale(params["Conv_1"]["kernel"], grid=(32,64), kernel_size=(3,3))
st.write("The model and its details are at https://huggingface.co/PrakhAI/CatVsDog") |