retinal.py

import gradio as gr
import torch
from torchvision import transforms, models
from PIL import Image
import numpy as np

# Recreate the model architecture (change to convnext_base or convnext_large as needed)
model = models.convnext_base(pretrained=False)  # or use convnext_large(pretrained=False) if that was used
model.classifier[2] = torch.nn.Linear(model.classifier[2].in_features, 8)  # 8 classes

# Load the saved weights
state_dict = torch.load("convnext_oct_model.pth", map_location=torch.device("cpu"))
model.load_state_dict(state_dict)
model.eval()

# Class labels
CLASS_LABELS = {
    0: "AMD",
    1: "CNV",
    2: "CSR",
    3: "DME",
    4: "DR",
    5: "DRUSEN",
    6: "MH",
    7: "NORMAL"
}

# Preprocessing function
def preprocess_image(image):
    preprocess = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    image = Image.fromarray(image.astype('uint8')).convert("RGB")
    image = preprocess(image)
    return image.unsqueeze(0)

# Prediction function
def predict_image(image):
    image_tensor = preprocess_image(image)
    with torch.no_grad():
        outputs = model(image_tensor)
        predicted = torch.max(outputs, 1)[1]
    class_idx = predicted.item()
    confidence = torch.nn.functional.softmax(outputs, dim=1)[0][class_idx].item()
    return CLASS_LABELS.get(class_idx, "Unknown"), round(confidence, 4)

# Gradio Interface
interface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(),
    outputs=[gr.Text(label="Prediction"), gr.Text(label="Confidence")],
    title="Retinal Image Classification",
    description="Upload a retinal image to predict the condition using a ConvNeXt model.",
    allow_flagging="never"
)
interface.api_name = "/predict"

# Launch the interface with a public link
if __name__ == "__main__":
    interface.launch(share=True)