Update app.py

app.py

@@ -7,6 +7,8 @@ import PIL
 
 # class map > y con esto done;
 
+class_map = ClassMap(['raccoon','banana'])
+
 model_2 = models.torchvision.retinanet.model(
     backbone=models.torchvision.retinanet.backbones.resnext50_32x4d_fpn (pretrained=True),
     num_classes=len(class_map)
@@ -23,20 +25,30 @@ model_2.load_state_dict(state_dict)
 
 # use test img:
 
-img = PIL.Image.open('test.jpg')
-
 infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size),tfms.A.Normalize()])
 
-pred_dict_2  = models.torchvision.retinanet.fastai.end2end_detect(
+def predict(img):
     
-    img, 
-    infer_tfms,
-    model_2.to("cpu"), 
-    class_map=class_map, 
-    detection_threshold=0.5
-)
+    img = PIL.Image.fromarray(img, "RGB")
+    
+    pred_dict_2  = models.torchvision.retinanet.fastai.end2end_detect(
+    
+        img, 
+        infer_tfms,
+        model_2.to("cpu"), 
+        class_map=class_map, 
+        detection_threshold=0.5
+    )
+    
+    return pred_dict["img"]
+    
+gr.Interface(
+    fn=predict, 
+    inputs=gr.inputs.Image(shape=(128, 128)), 
+    outputs=[gr.outputs.Image(type="pil", label="VFNet Inference")],
+    examples=['raccoon-test_1.jpg','raccoon-test_2.jpg']
+).launch(share=False)
+
 
-# modelo 2 -> construido:
 
-res_img = pred_dict_2['img']