Added Hot Dog Demo Project

App.py

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+import gradio as gr
+from transformers import pipeline
+
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+
+print("it is working")
+print("that is super exiciting")
+
+def predict(image):
+    predictions = pipeline(image)
+    return {p["label"]:p["score"] for p in predictions}
+
+gr.Interface(predict,
+      inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
+      outputs=gr.outputs.Label(num_top_classes=2),
+      title="Hot Dog? Or Not?",
+      ).launch()    
+
+
+def greet(name):
+    return "Hello " + name + "!"
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+    
+if __name__ == "__main__":
+    demo.launch()  

flagged/log.csv

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+Upload hot dog candidate,output,flag,username,timestamp
+C:\hotdog\flagged\Upload hot dog candidate\fdf3d49cc1e36299d924079acf6242fb821357b1\tmpxz6qs6ro.jpg,C:\hotdog\flagged\output\tmpa5n3d7fr.json,,,2023-06-23 22:56:24.825695

flagged/output/tmpa5n3d7fr.json

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+{"label": "hot dog", "confidences": [{"label": "hot dog", "confidence": 0.6445047855377197}, {"label": "not hot dog", "confidence": 0.3554952144622803}]}

requirements.txt

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+transformers
+torch