app.py

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()