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import gradio as gr
import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image
import os
# Define the same custom residual block and EfficientNetWithNovelty model
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
# Skip connection
self.skip = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
self.skip_bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
identity = self.skip(x)
x = self.relu(self.bn1(self.conv1(x)))
x = self.bn2(self.conv2(x))
x += identity # Add skip connection
x = self.relu(x)
return x
class EfficientNetWithNovelty(nn.Module):
def __init__(self, num_classes):
super(EfficientNetWithNovelty, self).__init__()
# Load pre-trained EfficientNet-B0 model
self.model = models.efficientnet_b0(pretrained=True)
# Modify the final classifier layer for our number of classes
self.model.classifier[1] = nn.Linear(self.model.classifier[1].in_features, num_classes)
# Add the custom residual block after the EfficientNet feature extractor
self.residual_block = ResidualBlock(1280, 1280) # 1280 is the output channels from EfficientNet B0
def forward(self, x):
# Pass through the EfficientNet feature extractor
x = self.model.features(x) # Access feature extraction part
# Pass through the custom residual block
x = self.residual_block(x)
# Flatten the output to feed into the classifier
x = x.mean([2, 3]) # Global Average Pooling
x = self.model.classifier(x) # Pass through the final classifier layer
return x
# Load the model checkpoint
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
num_classes = 10 # Number of classes as per your dataset
model = EfficientNetWithNovelty(num_classes)
checkpoint = torch.load('best_model2.pth')
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
# Define image transformations for preprocessing
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# Define the class labels explicitly
class_labels = [
"KNUCKLE",
"LEGSPIN",
"OFFSPIN",
"OUTSWING",
"STRAIGHT",
"BACK_OF_HAND",
"CARROM",
"CROSSSEAM",
"GOOGLY",
"INSWING"
]
# Prediction function
def predict(image):
# Preprocess image
image = Image.fromarray(image) # Convert numpy array to PIL Image if it's from Gradio
image = transform(image).unsqueeze(0).to(device)
# Get model predictions
with torch.no_grad():
output = model(image)
_, predicted = torch.max(output, 1)
# Get predicted class label
predicted_label = class_labels[predicted.item()]
return predicted_label
# Gradio interface
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="numpy", label="Upload Cricket Grip Image"),
outputs=gr.Textbox(label="Predicted Grip Type"),
live=True
)
if __name__ == "__main__":
iface.launch()
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