Update app.py

app.py

@@ -1,49 +1,48 @@
-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)
+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)