Update app.py

app.py

@@ -1,29 +1,3 @@
-# import gradio as gr
-# import cv2
-# import torch
-
-# # Load YOLOv7 model
-# model = torch.hub.load('models', 'custom', 'models/100epoch.pt',force_reload=True, source='local',trust_repo=True)
-# model.eval()
-
-# def process_image(input_image):
-#     # Perform inference on the input image
-#     results = model(input_image)
-#     img = results.render()[0]
-
-#     return img
-
-# iface = gr.Interface(
-#     fn=process_image,
-#     inputs=gr.components.Image(type='pil', label="Input Image"),
-#     outputs=gr.components.Image(type='pil', label="Processed Image"),
-#     live=True,
-#     title="YOLOv7 Object Detection",
-#     description="Upload an image and see the object detection results."
-# )
-
-# if __name__ == '__main__':
-#     iface.launch()
 import gradio as gr
 import cv2
 import torch
@@ -60,8 +34,15 @@ iface = gr.Interface(
     inputs=gr.components.Image(type='pil', label="Input Image"),
     outputs=[gr.components.Image(type='pil', label="Processed Image"), gr.components.Textbox(label="Detected Cancer Type")],
     live=True,
-    title="Lung Cancer Detection",
-    description="Upload an image and see the object detection results."
+    title="Lung Cancer Detection App",
+    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.""
 )
 
 if __name__ == '__main__':