Create app.py

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+import torch
+from torchvision import transforms
+from PIL import Image
+import numpy as np
+
+# Load the trained model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = YourModel()  # Replace 'YourModel' with your actual model class
+model.load_state_dict(torch.load('D:/Dataset/Cricket Bowl  Grip/final_model.pth'))
+model.to(device)
+model.eval()
+
+# Define the transformation to be applied to the input image
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # Resize image to fit your model's input size
+    transforms.ToTensor(),  # Convert image to tensor
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  # Example normalize values for ImageNet
+])
+
+# Define a function for making predictions
+def predict(image):
+    image = Image.fromarray(image)  # Convert numpy array to PIL Image
+    image = transform(image).unsqueeze(0)  # Apply transformations and add batch dimension
+    image = image.to(device)
+    
+    with torch.no_grad():
+        outputs = model(image)
+        _, predicted = torch.max(outputs, 1)
+        
+    # Map predicted label to class name
+    class_names = ['OUTSWING', 'STRAIGHT', 'BACK_OF_HAND', 'CARROM', 'CROSSSEAM', 
+                   'GOOGLY', 'INSWING', 'KNUCKLE', 'LEGSPIN', 'OFFSPIN']
+    predicted_label = class_names[predicted.item()]
+    
+    return predicted_label
+
+# Create the Gradio Interface
+iface = gr.Interface(fn=predict, 
+                     inputs=gr.Image(type="numpy"),  # Accepts image input
+                     outputs=gr.Text(),  # Output the predicted class label
+                     live=True)  # live=True enables prediction while image is being uploaded
+
+# Launch the interface
+iface.launch()