add app py

app.py

@@ -0,0 +1,55 @@
+import torch
+import clip
+from PIL import Image
+import gradio as gr
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = clip.load("ViT-B/32", device=device)
+
+def hotornot(image, gender):
+    image = Image.fromarray(image.astype("uint8"), "RGB")
+    image = preprocess(image).unsqueeze(0).to(device)
+    positive_terms = [f'a hot {gender}', f'a beautiful {gender}', f'an attractive {gender}']
+    negative_terms = [f'a gross {gender}', f'an ugly {gender}', f'a hideous {gender}']
+
+    pairs = list(zip(positive_terms, negative_terms))
+
+    def evaluate(terms):
+        text = clip.tokenize(terms).to(device)
+
+        with torch.no_grad():
+            logits_per_image, logits_per_text = model(image, text)
+            probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+            return probs[0]
+
+    probs = [evaluate(pair) for pair in pairs]
+    
+    positive_probs = [prob[0] for prob in probs]
+    negative_probs = [prob[1] for prob in probs]
+
+    print("+:", positive_probs)
+    print("-:", negative_probs)
+
+    hot_score = sum(positive_probs)/len(positive_probs)
+    ugly_score = sum(negative_probs)/len(negative_probs)
+    print(hot_score, ugly_score)
+    composite = ((hot_score - ugly_score)+1) * 50
+    composite = round(composite, 2)
+    return composite
+
+iface = gr.Interface(
+    fn=hotornot,
+    inputs=[
+        gr.inputs.Image(label="Image"),
+        gr.inputs.Dropdown(
+            [
+                'person', 'man', 'woman'
+            ],
+            default='person',
+        )
+    ],
+    outputs="number",
+    title="Hot or Not",
+    description="A simple hot or not app using OpenAI's CLIP model.",
+)
+iface.launch()