Add : Application file and Req. file

app.py

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+import torch
+import requests
+import gradio as gr
+from PIL import Image
+
+from transformers import AutoImageProcessor, ResNetForImageClassification
+
+target_folder = "JungminChung/India_ResNet"
+
+def load_model_and_preprocessor(target_folder):
+    model = ResNetForImageClassification.from_pretrained(target_folder)
+    image_processor = AutoImageProcessor.from_pretrained(target_folder)
+    return model, image_processor
+
+def fetch_image(url):
+    headers = {
+        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36'
+    }
+    image_raw = requests.get(url, headers=headers, stream=True).raw
+    image = Image.open(image_raw)
+
+    return image
+
+def infer_image(image, model, image_processor, k):
+    processed_img = image_processor(images=image.convert("RGB"), return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**processed_img)
+        logits = outputs.logits
+    
+    prob = torch.nn.functional.softmax(logits, dim=-1)
+    topk_prob, topk_indices = torch.topk(prob, k=k)   
+    
+    res = ""
+    for idx, (prob, index) in enumerate(zip(topk_prob[0], topk_indices[0])):
+        res += f"{idx+1}. {model.config.id2label[index.item()]:<15} ({prob.item()*100:.2f} %) \n" 
+    return res
+
+def infer(url, k, target_folder=target_folder):
+    try : 
+        image = fetch_image(url)
+        model, image_processor = load_model_and_preprocessor(target_folder)
+        res =  infer_image(image, model, image_processor, k)
+    except : 
+        image = Image.new('RGB', (224, 224))
+        res = "이미지를 불러오는데 문제가 있나봐요. 다른 이미지 url로 다시 시도해주세요."
+    return image, res 
+
+demo = gr.Interface(
+    fn=infer,
+    inputs=[
+        gr.Textbox(value="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRpE-UHBp8ZufNUd3BKw8gtIxSe3IUwspOfqw&s", 
+                   label="Image URL"),
+        gr.Slider(minimum=0, maximum=20, step=1, value=3, label="상위 몇개까지 보여줄까요?")
+    ],
+    outputs=[
+        gr.Image(type="pil", label="입력 이미지"),
+        gr.Textbox(label="종류 (확률)")
+    ],
+)
+
+demo.launch()
+# demo.launch(share=True)

requirements.txt

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+torch
+transformers
+Pillow