Create app.py

app.py

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+from transformers import AutoProcessor, AutoModel
+import torch
+import gradio as gr
+
+processor = AutoProcessor.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K")
+
+model = AutoModel.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K")
+
+labels = [f'a {emotion} face' for emotion in ['sad', 'happy', 'scared', 'angry', 'surprised', 'disgusted', 'neutral']]
+
+text_inputs = processor(text=labels, return_tensors='pt')
+text_embeds = model.get_text_features(**text_inputs)
+text_embeds /= text_embeds.norm(p=2, dim=-1, keepdim=True)
+
+def classify(image):
+  image_inputs = processor(images=image, return_tensors='pt')
+  image_embeds = model.get_image_features(**image_inputs)
+  image_embeds /= image_embeds.norm(p=2, dim=-1, keepdim=True)
+  logits_per_image = torch.matmul(image_embeds, text_embeds.t()) * model.logit_scale.exp()
+  probs = logits_per_image.softmax(dim=1).squeeze(0).tolist()
+  return dict(zip(labels, probs))
+
+demo = gr.Interface(
+    fn=classify,
+    inputs=gr.Image(type='pil', source='webcam', streaming=True),
+    outputs=gr.Label()
+)
+
+demo.launch()