Update app.py

app.py

@@ -3,21 +3,26 @@ import yolov5
 import os
 from transformers import pipeline
 
-ImageClassifier = pipeline(task="image-classification", model="")
+#ImageClassifier = pipeline(task="image-classification", model="")
 
 model = yolov5.load('./gentle-meadow.pt', device='cpu')
 
 def predict(image):
-  inp = transforms.ToTensor()(inp).unsqueeze(0)
-  with torch.no_grad():
-    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
-    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    
-  return confidences
+   results = model([image], size=224)
+    #predictions = imageClassifier(image)
+    # classMappings = {
+    #     'police': "Police / Authorized Personnel",
+    #     'public': 'Unauthorized Person'
+    # }
+    # output = {}
+    # for item in predictions:
+    #     output[classMappings[item['label']]] = item['score']
+
+    return results.render()[0]
 
 demo = gr.Interface(fn=predict, 
              inputs=gr.inputs.Image(type="pil"),
-             outputs=gr.outputs.Label(num_top_classes=3),
-             examples=[["cheetah.jpg"]],
+             outputs=gr.outputs.Image(type="pil"),
              )
              
 demo.launch()