Developed simple demo for XAI

.gitignore

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+venv/
+*.jpg
+*.jpeg
+*.png
+flagged/
+*.DS_Store
+*__pycache__/
+*.vs/
+*.idea/
+gradio_cached_examples/

README.md

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-# vit-explainer
+---
+title: 'vit-explainer'
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+app_port: 7860
+emoji: 🔥
+pinned: false
+license: mit
+app_file: app.py
+---
+
+# vit-explainer

app.py

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+import requests
+import re
+
+import gradio as gr
+import numpy as np
+from torch import topk
+from torch.nn.functional import softmax
+from transformers import ViTImageProcessor, ViTForImageClassification
+from transformers_interpret import ImageClassificationExplainer
+
+
+def load_label_data():
+    file_url = "https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt"
+    response = requests.get(file_url)
+    labels = []
+    pattern = '["\'](.*?)["\']'
+    for line in response.text.split('\n'):
+        try:
+            tmp = re.findall(pattern, line)[0]
+            labels.append(tmp)
+        except IndexError:
+            pass
+    return labels
+
+
+class WebUI:
+    def __init__(self):
+        super().__init__()
+        self.nb_classes = 10
+        self.processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
+        self.model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
+        self.labels = load_label_data()
+    
+    def run_model(self, image):
+        inputs = self.processor(images=image, return_tensors="pt")
+        outputs = self.model(**inputs)
+        outputs = softmax(outputs.logits, dim=1)
+        outputs = topk(outputs, k=self.nb_classes)
+        return outputs
+
+    def classify_image(self, image):
+        top10 = self.run_model(image)
+        return {self.labels[top10[1][0][i]]: float(top10[0][0][i]) for i in range(self.nb_classes)}
+
+    def explain_pred(self, image):
+        image_classification_explainer = ImageClassificationExplainer(model=self.model, feature_extractor=self.processor)
+        saliency = image_classification_explainer(image)
+        saliency = np.squeeze(np.moveaxis(saliency, 1, 3))
+        saliency[saliency >= 0.05] = 0.05
+        saliency[saliency <= -0.05] = -0.05
+        return saliency
+    
+    def run(self):
+        examples=[
+            ['https://github.com/andreped/INF1600-ai-workshop/releases/download/Examples/cat.jpg'],
+            ['https://github.com/andreped/INF1600-ai-workshop/releases/download/Examples/dog.jpeg'],
+        ]
+        with gr.Blocks() as demo:
+            with gr.Row():
+                image = gr.Image(height=512)
+                label = gr.Label(num_top_classes=self.nb_classes)
+                saliency = gr.Image(height=512, label="saliency map", show_label=True)
+
+                with gr.Column(scale=0.2, min_width=150):
+                    run_btn = gr.Button("Run analysis", variant="primary", elem_id="run-button")
+
+                    run_btn.click(
+                        fn=lambda x: self.explain_pred(x),
+                        inputs=image,
+                        outputs=saliency,
+                    )
+
+                    run_btn.click(
+                        fn=lambda x: self.classify_image(x),
+                        inputs=image,
+                        outputs=label,
+                    )
+
+                    gr.Examples(
+                        examples=[
+                            ['https://github.com/andreped/INF1600-ai-workshop/releases/download/Examples/cat.jpg'],
+                            ['https://github.com/andreped/INF1600-ai-workshop/releases/download/Examples/dog.jpeg'],
+                        ],
+                        inputs=image,
+                        outputs=image,
+                        fn=lambda x: x,
+                        cache_examples=True,
+                    )
+        
+        demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)
+
+
+def main():
+    ui = WebUI()
+    ui.run()
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+gradio
+torch
+transformers
+transformers-interpret

workflows/deploy.yml

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+name: Deploy
+on:
+  push:
+    branches: [ main ]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push https://andreped:[email protected]/spaces/andreped/vit-explainer main