Update app.py

app.py

@@ -25,8 +25,121 @@ blip_model = blip_decoder(pretrained=blip_model_url, image_size=blip_image_eval_
 blip_model.eval()
 blip_model = blip_model.to(device)
 
-def greet(name):
-    return "hi " + name + "!!"
+def generate_caption(pil_image):
+    gpu_image = transforms.Compose([
+        transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
+        transforms.ToTensor(),
+        transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
+    ])(pil_image).unsqueeze(0).to(device)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch(share=True)
+    with torch.no_grad():
+        caption = blip_model.generate(gpu_image, sample=False, num_beams=3, max_length=20, min_length=5)
+    return caption[0]
+
+def load_list(filename):
+    with open(filename, 'r', encoding='utf-8', errors='replace') as f:
+        items = [line.strip() for line in f.readlines()]
+    return items
+
+def rank(model, image_features, text_array, top_count=1):
+    top_count = min(top_count, len(text_array))
+    text_tokens = clip.tokenize([text for text in text_array]).cuda()
+    with torch.no_grad():
+        text_features = model.encode_text(text_tokens).float()
+    text_features /= text_features.norm(dim=-1, keepdim=True)
+
+    similarity = torch.zeros((1, len(text_array))).to(device)
+    for i in range(image_features.shape[0]):
+        similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
+    similarity /= image_features.shape[0]
+
+    top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)  
+    return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]
+    
+def interrogate(cover):
+    image = Image.fromarray(cover)
+    #image = cover
+    models = models1
+    #caption = generate_caption(Image.fromarray(cover))
+    caption = generate_caption(image)
+    if len(models) == 0:
+        #print(f"\n\n{caption}")
+        return
+
+    table = []
+    bests = [[('',0)]]*5
+    for model_name in models:
+        #print(f"Interrogating with {model_name}...")
+        model, preprocess = clip.load(model_name)
+        model.cuda().eval()
+
+        images = preprocess(image).unsqueeze(0).cuda()
+        with torch.no_grad():
+            image_features = model.encode_image(images).float()
+        image_features /= image_features.norm(dim=-1, keepdim=True)
+
+        ranks = [
+            rank(model, image_features, mediums),
+            rank(model, image_features, ["by "+artist for artist in artists]),
+            rank(model, image_features, trending_list),
+            rank(model, image_features, movements),
+            rank(model, image_features, flavors, top_count=3)
+        ]
+
+        for i in range(len(ranks)):
+            confidence_sum = 0
+            for ci in range(len(ranks[i])):
+                confidence_sum += ranks[i][ci][1]
+            if confidence_sum > sum(bests[i][t][1] for t in range(len(bests[i]))):
+                bests[i] = ranks[i]
+
+        row = [model_name]
+        for r in ranks:
+            row.append(', '.join([f"{x[0]} ({x[1]:0.1f}%)" for x in r]))
+
+        table.append(row)
+
+        del model
+        gc.collect()
+    #display(pd.DataFrame(table, columns=["Model", "Medium", "Artist", "Trending", "Movement", "Flavors"]))
+
+    flaves = ', '.join([f"{x[0]}" for x in bests[4]])
+    medium = bests[0][0][0]
+    if caption.startswith(medium):
+        return(f"{caption} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}")
+        #print(f"{caption} {bests[3][0][0]}, {flaves}")
+    else:
+        return(f"{caption}, {medium} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}")
+        #print(f"{caption} {bests[3][0][0]}, {flaves}")
+        
+data_path = "/clip-interrogator/data/"
+
+artists = load_list(os.path.join(data_path, 'artists.txt'))
+flavors = load_list(os.path.join(data_path, 'flavors.txt'))
+mediums = load_list(os.path.join(data_path, 'mediums.txt'))
+movements = load_list(os.path.join(data_path, 'movements.txt'))
+
+sites = ['Artstation', 'behance', 'cg society', 'cgsociety', 'deviantart', 'dribble', 'flickr', 'instagram', 'pexels', 'pinterest', 'pixabay', 'pixiv', 'polycount', 'reddit', 'shutterstock', 'tumblr', 'unsplash', 'zbrush central']
+trending_list = [site for site in sites]
+trending_list.extend(["trending on "+site for site in sites])
+trending_list.extend(["featured on "+site for site in sites])
+trending_list.extend([site+" contest winner" for site in sites])
+
+models1 = ['ViT-B/32']
+
+width = 130
+height = 180
+
+cover = gr.inputs.Image(shape=(width, height), label='Upload cover image to classify')
+label = gr.outputs.Label(label='Model prediction')
+
+examples=["00064.jpg","00068.jpg", "00069.jpg"]
+#gr.Interface(fn=interrogate,inputs=[gr.Image()],output_label,examples=examples).launch()
+#gr.Interface(fn=interrogate,inputs=gr.Image(),outputs=gr.outputs.Label(),examples=examples).launch()
+gr.Interface(fn=interrogate,inputs=cover,outputs=label,examples=examples).launch(share=True)
+
+#def greet(name):
+    #return "hi " + name + "!!"
+
+#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+#iface.launch(share=True)