| import torch | |
| import torch.nn as nn | |
| import torchvision.transforms as transforms | |
| from PIL import Image | |
| import gradio as gr | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model = torch.hub.load('pytorch/vision:v0.10.0', 'inception_v3', pretrained=True) | |
| n_classes = 10 | |
| model.fc = nn.Linear(model.fc.in_features, n_classes) | |
| model = model.to(device) | |
| model.load_state_dict(torch.load("NumtaDB_Classifier_Model.pth", map_location=device)) | |
| model.eval() | |
| transform = transforms.Compose([ | |
| transforms.Resize((299, 299)), | |
| transforms.Grayscale(num_output_channels=3), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| ]) | |
| label_name = ["Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Nine", "Ten"] | |
| def predict(image): | |
| if not isinstance(image, Image.Image): | |
| image = Image.fromarray(image) | |
| image_tensor = transform(image).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| outputs = model(image_tensor) | |
| probs = torch.softmax(outputs, dim=1) | |
| predictions = {label_name[i]: float(probs[0][i]) for i in range(len(label_name))} | |
| return predictions | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(label="Upload Image"), | |
| outputs=gr.Label(num_top_classes=len(label_name)), | |
| title="BanglaDigitPro: Advanced Bengali Numeral Recognition", | |
| description="Upload an image of a handwritten Bangla digit to classify it.", | |
| examples=[["example_1.png"], ["example_2.png"], ["example_3.png"], ["example_4.png"], ["example_5.png"]]) | |
| iface.launch(share=True) | |