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import gradio as gr
import torch
import torchvision.transforms as transforms
from PIL import Image
import torch.nn as nn
import os
# ✅ Define Lightweight CNN Model (Same as trained)
class SmallCNN(nn.Module):
def __init__(self):
super(SmallCNN, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1) # Reduced filters
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1) # Reduced filters
self.fc1 = nn.Linear(32 * 8 * 8, 10) # 10-class classification (CIFAR-10)
def forward(self, x):
x = torch.relu(self.conv1(x))
x = torch.max_pool2d(x, 2)
x = torch.relu(self.conv2(x))
x = torch.max_pool2d(x, 2)
x = x.view(x.size(0), -1)
x = self.fc1(x)
return x
# ✅ Load the trained model from Hugging Face Space
model_path = os.path.join(os.getenv("SPACE_ROOT", ""), "light_cnn_model.pth")
# ✅ Initialize model and load weights
model = SmallCNN()
model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
model.eval()
# ✅ Define Image Transformation
transform = transforms.Compose([
transforms.Resize((32, 32)), # Resize image to 32x32 pixels
transforms.ToTensor(), # Convert image to tensor
])
# ✅ Define Prediction Function
def predict(image):
image = transform(image).unsqueeze(0) # Convert image to tensor and add batch dimension
with torch.no_grad():
output = model(image) # Forward pass through model
prediction = torch.argmax(output, dim=1).item() # Get predicted class
return f"Predicted Class: {prediction}"
# ✅ Create Gradio Interface
interface = gr.Interface(
fn=predict, # Function to process image
inputs=gr.Image(type="pil"), # User uploads an image
outputs="text", # Model returns a text output
title="Lightweight CNN Image Classification",
description="Upload an image to classify using the trained CNN model.",
)
# ✅ Launch the Gradio App
interface.launch()
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