Duplicate from abidlabs/image-classifier

Co-authored-by: Abubakar Abid <[email protected]>

.gitattributes

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README.md

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+---
+title: Image Classifier
+emoji: 🏢
+colorFrom: green
+colorTo: gray
+sdk: gradio
+sdk_version: 3.11.0
+app_file: app.py
+pinned: false
+duplicated_from: abidlabs/image-classifier
+---
+
+# Configuration
+
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`sdk_version` : _string_  
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

app.py

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+from transformers import ViTFeatureExtractor, ViTForImageClassification
+from PIL import Image
+import torch
+import torch.nn.functional as F
+import time
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
+model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224').to(device)
+
+def predict(image):
+    inputs = feature_extractor(images=image, return_tensors="pt").to(device)
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_prob = F.softmax(logits, dim=-1).detach().cpu().numpy().max()
+    predicted_class_idx = logits.argmax(-1).item()
+    label = model.config.id2label[predicted_class_idx].split(",")[0]
+    time.sleep(2)
+    return {label: float(predicted_class_prob)}
+ 
+import gradio as gr
+
+gr.Interface(predict, gr.Image(type="pil"), "label").queue(concurrency_count=1).launch()

requirements.txt

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+torch
+transformers