Update app.py

app.py

@@ -1,14 +1,9 @@
-import gradio as gr
-from ultralytics import YOLO
 from PIL import Image
-
-
+import gradio as gr
 
 model_path = 'new_data_improved_object_detector.pt'
-
 model = YOLO(model_path)
 
-
 def predict_image(img, conf_threshold, iou_threshold):
     """Predicts and plots labeled objects in an image using YOLOv8 model with adjustable confidence and IOU thresholds."""
     # Convert the input image to grayscale
@@ -16,44 +11,61 @@ def predict_image(img, conf_threshold, iou_threshold):
 
     results = model.predict(
         source=img,
-        conf =0.4,
-        iou=0.6,
+        conf=conf_threshold,
+        iou=iou_threshold,
         show_labels=True,
         show_conf=True,
         imgsz=640,
     )
 
+    im_arrays = []
+    all_model_result=[]
+    all_xywh = []
+    all_clss = []
+    all_names = []
+    all_confidence = []
+    all_xyxy =[]
+
     for r in results:
+        model_result =results[0]
+        xywh = r.boxes.xywh.cpu().tolist()
+        clss = r.boxes.cls.cpu().tolist()
+        names = [r.names[cls] for cls in clss]  # Convert class indices to names
+        confidence = r.boxes.conf.cpu().tolist()
+        xyxy = r.boxes.cpu().xyxy.tolist()
+
         im_array = r.plot()
         im = Image.fromarray(im_array[..., ::-1])
+        im_arrays.append(im)
         
-    # detected_result = {"results":results}
-
-    return im,results
+        all_model_result.extend(model_result)
+        all_xywh.extend(xywh)
+        all_clss.extend(clss)
+        all_names.extend(names)
+        all_confidence.extend(confidence)
+        all_xyxy.extend(xyxy)
 
+    return im_arrays, model_result,all_xywh, all_clss, all_names, all_confidence,xyxy
 
 iface = gr.Interface(
-    
     fn=predict_image,
     inputs=[
         gr.Image(type="pil", label="Upload Image"),
         gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
         gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
     ],
-    # outputs=gr.Image(type="pil", label="Result"),
-    
-    outputs=[gr.Image(type="pil", label="Result"),gr.JSON(label="Detection Results")
-             ],
-    
-    title="Compliance Checker",
+    outputs=[
+        gr.Gallery(label="Result Images"),
+        gr.JSON(label="Detection Bounding Boxes (model_result)"),
+        gr.JSON(label="Detection Bounding Boxes (xywh)"),
+        gr.JSON(label="Detection Class Indices"),
+        gr.JSON(label="Detection Class Names"),
+        gr.JSON(label="Detection Confidence Scores"),
+        gr.JSON(label="Detection Bounding Boxes (xyxy)")
+    ],
+    title="Ultralytics Gradio",
     description="Upload images for inference. The Ultralytics YOLOv8n model is used by default.",
-    examples = [
-            ['1.jpg'],
-            ['2.jpg'],
-            ['3.jpg']
-      ]
 )
 
-
-# Launch the Gradio interface in debug mode with queue enabled
-iface.launch(debug=True, share=False).queue()
+if __name__ == "__main__":
+    iface.launch()