| import gradio as gr |
| import cv2 |
| import torch |
|
|
| |
| model = torch.hub.load('models', 'custom', 'models/100epoch.pt', force_reload=True, source='local', trust_repo=True) |
| model.eval() |
|
|
| |
| label_mapping = { |
| 'A': 'Adenocarcinoma', |
| 'B': 'Small Cell Carcinoma', |
| 'E': 'Large Cell Carcinoma', |
| 'G': 'Squamous Cell Carcinoma' |
| } |
|
|
| def process_image(input_image): |
| |
| results = model(input_image) |
| img = results.render()[0] |
| if results.pred is not None and len(results.pred[0]) > 0: |
| detection = results.pred[0] |
| class_index = int(detection[0, -1]) |
| print(class_index) |
| class_label = model.names[class_index] |
| mapped_label = label_mapping.get(class_label, "Unknown") |
| else: |
| mapped_label = "No cancer detected" |
| |
| return img, mapped_label |
|
|
|
|
| iface = gr.Interface( |
| fn=process_image, |
| inputs=gr.components.Image(type='pil', label="Input Image").style(height=280), |
| outputs=[gr.components.Image(type='pil', label="Processed Image").style(height=280), gr.components.Textbox(label="Detected Cancer Type")], |
| live=True, |
| title="Lung Cancer Detector ⚕️", |
| description="The AI model was trained to detect the following types of lung cancer:\n" |
| "1. Adenocarcinoma (A)\n" |
| "2. Small Cell Carcinoma (B)\n" |
| "3. Large Cell Carcinoma (E)\n" |
| "4. Squamous Cell Carcinoma (G)\n\n" |
| "How to Use :\n" |
| "1. Upload a CT scan image of a patient's lungs.\n" |
| "2. The app will display the predicted type of lung cancer.", |
| theme=gr.themes.Monochrome(font=[gr.themes.GoogleFont("Noto Serif"), "Preahvihear", "sans-serif"]) |
| |
| ) |
|
|
| if __name__ == '__main__': |
| iface.launch() |
