Update app.py

app.py

@@ -1,7 +1,59 @@
 import gradio as gr
+import torch
+from PIL import ImageDraw
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Model  
+model_path = 'model_torch.pt'
+model = torch.hub.load('Ultralytics/yolov5', 'custom', model_path, verbose = False)
+model.eval()
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+labels = model.names
+
+colors = ["red", "blue", "green", "yellow"]
+
+def detect_objects(image):
+
+    draw = ImageDraw.Draw(image)
+
+    detections = model(image)
+
+    for detection in detections.xyxy[0]:
+        
+        x1, y1, x2, y2, p, category_id = detection
+        x1, y1, x2, y2, category_id = int(x1), int(y1), int(x2), int(y2), int(category_id)
+        draw.rectangle((x1, y1, x2, y2), outline=colors[category_id], width=4)
+        draw.text((x1, y1), labels[category_id], colors[category_id])
+
+    return image
+
+
+demo = gr.Blocks()#(css=css)
+
+title = '# 3D print failures detection App'
+description = 'App for detect errors in the 3D printing'
+
+urls = ["https://c8.alamy.com/comp/J2AB4K/the-new-york-stock-exchange-on-the-wall-street-in-new-york-J2AB4K.jpg"]
+
+with demo:
+    gr.Markdown(title)
+    gr.Markdown(description)
+    
+    with gr.Tabs():
+        with gr.TabItem('Image Upload'):
+            with gr.Row():
+                img_input = gr.Image(type='pil')
+                img_output= gr.Image()
+                
+            #with gr.Row(): 
+            #    example_images = gr.Dataset(components=[img_input],
+            #                                samples=[[path.as_posix()]
+            #                                         for path in sorted(pathlib.Path('images').rglob('*.JPG'))])
+                
+            img_button = gr.Button('Detect')
+        
+
+    img_button.click(detect_objects,inputs=img_input,outputs=img_output)
+    
+
+if __name__ == "__main__":
+    demo.launch()