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app.py

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+import gradio as gr
+import torch
+import torchvision.models as models
+from huggingface_hub import hf_hub_download
+import json
+from PIL import Image
+import torchvision.transforms as transforms
+
+# Set device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load model weights and class labels
+weights_path = hf_hub_download(repo_id="AventIQ-AI/resnet18-cataract-detection-system", filename="cataract_detection_resnet18_quantized.pth")
+labels_path = hf_hub_download(repo_id="AventIQ-AI/resnet18-cataract-detection-system", filename="class_names.json")
+
+with open(labels_path, "r") as f:
+    class_labels = json.load(f)
+
+# Load model
+model = models.resnet18(pretrained=False)
+num_classes = len(class_labels)
+model.fc = torch.nn.Linear(in_features=512, out_features=num_classes)
+model.load_state_dict(torch.load(weights_path, map_location=device))
+model.to(device)
+model.eval()
+
+# Define transform
+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])
+])
+
+# Prediction function
+def predict(image):
+    image = transform(image).unsqueeze(0).to(device)  # Preprocess image
+    with torch.no_grad():
+        output = model(image)
+        _, predicted = torch.max(output, 1)
+    predicted_class = class_labels[predicted.item()]
+    return {predicted_class: 1.0}  # Confidence is assumed to be 1.0 for simplicity
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload an Eye Image"),
+    outputs=gr.Label(label="Cataract Detection Result"),
+    title="👁️ Cataract Detection System 🏥",
+    description="🔬 Upload an eye image, and the AI model will determine if cataract is present! 👓",
+    theme="huggingface",
+    allow_flagging="never",
+)
+
+demo.launch()

requirements.txt

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+torch
+transformers
+gradio
+sentencepiece
+torchvision
+huggingface_hub
+pillow
+numpy