test2

app.py

@@ -1,42 +1,38 @@
 import gradio as gr
 
 
-# from transformers import pipeline
+from transformers import pipeline
 
-# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
 
 
 
-# def predict(input_img):
-#     predictions = pipeline(input_img)
-#     return input_img, {p["label"]: p["score"] for p in predictions} 
+def predict(input_img):
+    predictions = pipeline(input_img)
+    return input_img, {p["label"]: p["score"] for p in predictions} 
 
-# gradio_app = gr.Interface(
-#     predict,
-#     inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
-#     outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-#     title="Hot Dog? Or Not?",
-# )
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
+    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    title="Hot Dog? Or Not?",
+)
 
 
 
+if __name__ == "__main__":
+    gradio_app.launch()
 
 #=================================================
 
 # import time
 # timenow = time.ctime()
-timenow = "10:10 AM"
 
-def greet(name):
-    return "Hello " + name + "!!" + f"Time now is {timenow}"
+# def greet(name):
+#     return "Hello " + name + "!!" + f"Time now is {timenow}"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 
-demo.launch()
+# demo.launch()
 
-# if __name__ == "__main__":
-#     if IsTutorial:
-#         demo.launch()
-#     else:
-#         gradio_app.launch()