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Blocks.py

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+import gradio as gr
+from transformers import pipeline
+
+classifier = pipeline(
+    "image-classification", 
+    model="google/vit-base-patch16-224"
+)
+
+def classify_image(image, top_k=3):
+    results = classifier(image)
+    sorted_results = sorted(results, 
+                            key=lambda x: x["score"], 
+                            reverse=True)
+    output_dict = {result["label"]: result["score"] 
+                   for result in sorted_results[:top_k]}
+    return output_dict
+
+with gr.Blocks() as demo:
+    with gr.Row(): 
+        gr.HTML("<h1 style='text-align: center;'>ViT 기반 이미지 분류 데모</h1>")
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(type="pil", label="이미지 업로드")
+        with gr.Column():
+            with gr.Row():
+                top_k = gr.Slider(minimum=1, maximum=10, 
+                                  value=3, step=1, label="상위 K개 결과")
+            with gr.Row():
+                classify = gr.Button('분류!') 
+    with gr.Row():
+        output = gr.Label(label="분류 결과")
+
+    classify.click(
+        fn=classify_image,
+        inputs=[image, top_k],
+        outputs=output
+    )
+
+demo.launch()

README.md

@@ -1,12 +1,6 @@
 ---
 title: GradioRecipe
-emoji: 🔥
-colorFrom: blue
-colorTo: blue
+app_file: Blocks.py
 sdk: gradio
-sdk_version: 5.12.0
-app_file: app.py
-pinned: false
+sdk_version: 5.11.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

components.py

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+import gradio as gr
+from transformers import pipeline
+
+classifier = pipeline(
+    "image-classification", 
+    model="google/vit-base-patch16-224"
+)
+
+def classify_image(image, top_k=3):
+    results = classifier(image)
+    sorted_results = sorted(results, 
+                            key=lambda x: x["score"], 
+                            reverse=True)
+    output_dict = {result["label"]: result["score"] 
+                   for result in sorted_results[:top_k]}
+    return output_dict
+
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=[
+        gr.Image(
+            type="pil", 
+            label="이미지 업로드"
+        ),  
+        gr.Slider(
+            minimum=1,
+            maximum=10, 
+            value=3,  
+            step=1,
+            label="상위 K개 결과"
+        ),  
+    ],
+    outputs=gr.Label(label="분류 결과"), 
+)
+
+demo.launch()

hello_gradio.py

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+import gradio as gr
+
+def greet(name):
+    return name + "님!, 반갑습니다. \nGradio의 세계로 오신것을 환영해요! :)"
+
+demo = gr.Interface(
+    fn=greet, 
+    inputs="text", 
+    outputs="text"
+)
+demo.launch(share=True) 

introduce.py

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+import gradio as gr 
+
+def do_introduce(name, age): 
+    return f'안녕하세요. 저는 {name}입니다. 저의 나이는 {age}살 입니다.'
+
+with gr.Blocks() as demo: 
+    with gr.Row(): 
+        with gr.Column(): 
+            name = gr.Text(label='이름')
+            age = gr.Text(label='나이')
+        with gr.Column(): 
+            intro = gr.Button('시작!') 
+    with gr.Row():
+        output = gr.Text(label='소개 문구') 
+
+    intro.click(
+        fn=do_introduce,
+        inputs=[name, age],
+        outputs=output
+    )
+
+demo.launch()

requirements.txt

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+gradio==5.11.0
+transformers==4.48.0
+torch