test1

app.py

@@ -1,39 +1,42 @@
 import gradio as gr
-from transformers import pipeline
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
+# from transformers import pipeline
 
+# 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} 
 
-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?",
-)
+
+# 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?",
+# )
 
 
 
 
 #=================================================
-IsTutorial = True
 
-import time
+# import time
+# timenow = time.ctime()
+timenow = "10:10 AM"
 
 def greet(name):
-    return "Hello " + name + "!!" + f"Time now is {time.ctime()}"
+    return "Hello " + name + "!!" + f"Time now is {timenow}"
 
 demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 
+demo.launch()
 
-
-if __name__ == "__main__":
-    if IsTutorial:
-        demo.launch()
-    else:
-        gradio_app.launch()
+# if __name__ == "__main__":
+#     if IsTutorial:
+#         demo.launch()
+#     else:
+#         gradio_app.launch()