Spaces:
Sleeping
Sleeping
File size: 2,047 Bytes
81a6137 56fd351 81a6137 d1d4583 81a6137 d1d4583 81a6137 d1d4583 95da1e4 56fd351 d1d4583 81a6137 d1d4583 81a6137 d1d4583 81a6137 d1d4583 81a6137 a0962d2 81a6137 d1d4583 81a6137 a8024f1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import gradio as gr
import torch
import numpy as np
from torchvision import transforms
from PIL import Image
# Load the model using PyTorch
model_path = "https://huggingface.co/immartian/improved_digits_recognition/resolve/main/pytorch_model.bin"
# Define your ImageClassifier model architecture (same as used during training)
class ImageClassifier(torch.nn.Module):
def __init__(self):
super().__init__()
self.model = torch.nn.Sequential(
torch.nn.Conv2d(1, 32, (3, 3)),
torch.nn.ReLU(),
torch.nn.Conv2d(32, 64, (3, 3)),
torch.nn.ReLU(),
torch.nn.Conv2d(64, 64, (3, 3)),
torch.nn.ReLU(),
torch.nn.AdaptiveAvgPool2d((1, 1)),
torch.nn.Flatten(),
torch.nn.Linear(64, 10)
)
def forward(self, x):
return self.model(x)
# Instantiate the model and load weights
model = ImageClassifier()
model.load_state_dict(torch.hub.load_state_dict_from_url(model_path))
model.eval()
# Gradio preprocessing and prediction pipeline
def predict_digit(image):
# Convert the numpy array (from gr.Sketchpad) to a PIL Image
image = Image.fromarray(image).convert('L') # Convert to grayscale
# Preprocess: resize to 28x28 and normalize
transform = transforms.Compose([
transforms.Resize((28, 28)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
img_tensor = transform(image).unsqueeze(0) # Add batch dimension
# Pass through the model
with torch.no_grad():
output = model(img_tensor)
predicted_label = torch.argmax(output, dim=1).item()
return f"Predicted Label: {predicted_label}"
# Create Gradio Interface
interface = gr.Interface(
fn=predict_digit,
inputs=gr.Sketchpad(), # Sketchpad for users to draw
outputs="text",
title="Digit Recognizer",
description="Draw a digit (0-9) and the model will predict the number!"
)
# Launch the app
if __name__ == "__main__":
interface.launch()
|