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README.md

@@ -1,10 +1,10 @@
 ---
-title: Ia Vit
-emoji: 🏃
-colorFrom: red
-colorTo: gray
+title: ViT ImageNet Classification
+emoji: 🔥
+colorFrom: pink
+colorTo: pink
 sdk: gradio
-sdk_version: 3.47.1
+sdk_version: 3.16.1
 app_file: app.py
 pinned: false
 ---

app.py

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+
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+
+import torch
+import gradio as gr
+
+from PIL import Image
+
+feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
+model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
+
+import os, glob
+
+examples_dir = './samples'
+example_files = glob.glob(os.path.join(examples_dir, '*.jpg'))
+
+def classify_image(image):
+
+    with torch.no_grad():
+        model.eval()
+
+        inputs = feature_extractor(images=image, return_tensors="pt")
+        outputs = model(**inputs)
+    
+    logits = outputs.logits
+    prob = torch.nn.functional.softmax(logits, dim=1)
+
+    top10_prob, top10_indices = torch.topk(prob, 10)
+    top10_confidences = {}
+    for i in range(10):
+        top10_confidences[model.config.id2label[int(top10_indices[0][i])]] = float(top10_prob[0][i])
+
+    return top10_confidences #confidences
+
+
+with gr.Blocks(title="ViT ImageNet Classification - ClassCat",
+            css=".gradio-container {background:mintcream;}"
+        ) as demo:
+    gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">ViT - ImageNet Classification</div>""")
+
+    with gr.Row(): 
+        input_image = gr.Image(type="pil", image_mode="RGB", shape=(224, 224))        
+        output_label=gr.Label(label="Probabilities", num_top_classes=3)
+
+    send_btn = gr.Button("Infer")
+    send_btn.click(fn=classify_image, inputs=input_image, outputs=output_label)
+
+    with gr.Row():
+        gr.Examples(['./samples/cat.jpg'], label='Sample images : cat', inputs=input_image)
+        gr.Examples(['./samples/cheetah.jpg'], label='cheetah', inputs=input_image)
+        gr.Examples(['./samples/hotdog.jpg'], label='hotdog', inputs=input_image)
+        gr.Examples(['./samples/lion.jpg'], label='lion', inputs=input_image)
+        #gr.Examples(example_files, inputs=input_image)
+
+#demo.queue(concurrency_count=3)
+demo.launch(debug=True)

requirements.txt

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+transformers
+torch