Update app.py

app.py

@@ -0,0 +1,23 @@
+import gradio as gr
+import yolov5
+import os
+from transformers import pipeline
+
+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
+
+demo = gr.Interface(fn=predict, 
+             inputs=gr.inputs.Image(type="pil"),
+             outputs=gr.outputs.Label(num_top_classes=3),
+             examples=[["cheetah.jpg"]],
+             )
+             
+demo.launch()