First commit

app.py

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+import os
+import traceback
+import gradio as gr
+import torch
+from torchvision.models import get_model
+from torchvision.transforms import v2
+from torchvision.transforms.functional import InterpolationMode
+
+
+# Imagenet-1k classes
+if not os.path.exists("imagenet_classes.txt"):
+    os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
+
+# Download an example image from the pytorch website
+if not os.path.exists("dog.jpg"):
+    torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
+
+
+# Function to load the model with custom weights
+def load_model(weights_path):
+    model = get_model("resnet50", num_classes=1000)
+    ckpt = torch.load(weights_path, map_location=torch.device("cpu"))
+    model.load_state_dict(ckpt["model_state_dict"])
+    model.eval()
+    return model
+
+
+# Function for making predictions and returning top 5 predictions with confidence
+def classify_image(image):
+    # Preprocess the input image
+    image = transform(image).unsqueeze(0)  # Add batch dimension
+
+    with torch.no_grad():
+        output = model(image)  # Get model output
+    
+    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+
+    # Read the categories
+    with open("imagenet_classes.txt", "r") as f:
+        categories = [s.strip() for s in f.readlines()]
+    
+    # Show top categories per image
+    top5_prob, top5_catid = torch.topk(probabilities, 5)
+    result = {}
+    for i in range(top5_prob.size(0)):
+        result[categories[top5_catid[i]]] = top5_prob[i].item()
+    return result
+
+# Define image transformation to match the model input
+transform = v2.Compose([
+    v2.Resize(256, interpolation=InterpolationMode.BILINEAR, antialias=True),
+    v2.CenterCrop(224),
+    v2.PILToTensor(),
+    v2.ToDtype(torch.float, scale=True),
+    v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    v2.ToPureTensor(),
+])
+
+# Path to the pre-trained model weights (should be set by the user)
+model_weights_path = "best.pth"
+model = load_model(model_weights_path)
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=classify_image,  # The function to run on input
+    inputs=gr.Image(type="pil"),  # Image input (in PIL format)
+    outputs=gr.Label(num_top_classes=5),  # Output will be the predicted top 5 classes with confidence scores
+    title = "Image Recognition using ResNet-50 trained on Imagenet-1K",
+    description = "<p style='text-align: center'> Gradio demo for ResNet, Deep residual networks pre-trained on ImageNet. To use it, simply upload your image, or click one of the examples to load them. </p>",
+    article = "<p style='text-align: center'> \
+        <a href='https://arxiv.org/abs/1512.03385' target='_blank'>Deep Residual Learning for Image Recognition</a> | \
+            <a href='https://github.com/KD1994/session-9-imagenet-resnet50' target='_blank'>Github Repo</a> \
+                </p>",
+    examples = [
+        ['dog.jpg']
+    ]
+)
+
+# Add error handling to launch
+try:
+    iface.launch(share=True)
+except Exception as e:
+    print(f"Error launching interface: {str(e)}")
+    print(traceback.format_exc()) 

best.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b6fdff2ad1f20d1c622f84375e1ec1dbdecd8c8f5488beade445bbfa6509e0fd
+size 204794470

requirement.txt

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+gradio==5.9.1
+torch==2.3.1
+torchvision==0.18.1