app.py

import gradio as gr
import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image

# 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 on CPU
device = torch.device('cpu')  # Ensure it's using CPU
num_classes = 10  # Number of classes as per your dataset
model = EfficientNetWithNovelty(num_classes)
checkpoint = torch.load('best_model2.pth', map_location=torch.device('cpu'))
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()