update

app.py

@@ -1,18 +1,13 @@
-import gradio as gr
 from transformers import pipeline
+classifier = pipeline('image-classification', model = "computer_partsclassifier-model")import numpy as np
 
-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 image_classifier(inp):
+  confidence_scores = np.random.rand(3)
+  confidence_scores/= np.sum(confidence_scores)
+  classes= ['cable','case', 'cpu']
+  result= {classes[i]: confidence_scores[i] for i in range(3)}
+  return result
 
-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 gradio as gr
+demo = gr.Interface(fn=image_classifier, inputs = "image", outputs = "label")
+demo.launch()