Create app.py

app.py

@@ -1,243 +1,7 @@
-import sys
-sys.path.append('src/blip')
-sys.path.append('src/clip')
-
-import clip
 import gradio as gr
-import hashlib
-import math
-import numpy as np
-import os
-import pickle
-import torch
-import torchvision.transforms as T
-import torchvision.transforms.functional as TF
-
-from models.blip import blip_decoder
-from PIL import Image
-from torch import nn
-from torch.nn import functional as F
-from tqdm import tqdm
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-print("Loading BLIP model...")
-blip_image_eval_size = 384
-blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth'        
-blip_model = blip_decoder(pretrained=blip_model_url, image_size=blip_image_eval_size, vit='large', med_config='./src/blip/configs/med_config.json')
-blip_model.eval()
-blip_model = blip_model.to(device)
-
-print("Loading CLIP model...")
-clip_model_name = 'ViT-L/14'
-clip_model, clip_preprocess = clip.load(clip_model_name, device=device)
-clip_model.to(device).eval()
-
-chunk_size = 2048
-flavor_intermediate_count = 2048
-
-
-class LabelTable():
-    def __init__(self, labels, desc):
-        self.labels = labels
-        self.embeds = []
-
-        hash = hashlib.sha256(",".join(labels).encode()).hexdigest()
-
-        os.makedirs('./cache', exist_ok=True)
-        cache_filepath = f"./cache/{desc}.pkl"
-        if desc is not None and os.path.exists(cache_filepath):
-            with open(cache_filepath, 'rb') as f:
-                data = pickle.load(f)
-                if data['hash'] == hash:
-                    self.labels = data['labels']
-                    self.embeds = data['embeds']
-
-        if len(self.labels) != len(self.embeds):
-            self.embeds = []
-            chunks = np.array_split(self.labels, max(1, len(self.labels)/chunk_size))
-            for chunk in tqdm(chunks, desc=f"Preprocessing {desc}" if desc else None):
-                text_tokens = clip.tokenize(chunk).to(device)
-                with torch.no_grad():
-                    text_features = clip_model.encode_text(text_tokens).float()
-                text_features /= text_features.norm(dim=-1, keepdim=True)
-                text_features = text_features.half().cpu().numpy()
-                for i in range(text_features.shape[0]):
-                    self.embeds.append(text_features[i])
-
-            with open(cache_filepath, 'wb') as f:
-                pickle.dump({"labels":self.labels, "embeds":self.embeds, "hash":hash}, f)
-    
-    def _rank(self, image_features, text_embeds, top_count=1):
-        top_count = min(top_count, len(text_embeds))
-        similarity = torch.zeros((1, len(text_embeds))).to(device)
-        text_embeds = torch.stack([torch.from_numpy(t) for t in text_embeds]).float().to(device)
-        for i in range(image_features.shape[0]):
-            similarity += (image_features[i].unsqueeze(0) @ text_embeds.T).softmax(dim=-1)
-        _, top_labels = similarity.cpu().topk(top_count, dim=-1)
-        return [top_labels[0][i].numpy() for i in range(top_count)]
-
-    def rank(self, image_features, top_count=1):
-        if len(self.labels) <= chunk_size:
-            tops = self._rank(image_features, self.embeds, top_count=top_count)
-            return [self.labels[i] for i in tops]
-
-        num_chunks = int(math.ceil(len(self.labels)/chunk_size))
-        keep_per_chunk = int(chunk_size / num_chunks)
-
-        top_labels, top_embeds = [], []
-        for chunk_idx in tqdm(range(num_chunks)):
-            start = chunk_idx*chunk_size
-            stop = min(start+chunk_size, len(self.embeds))
-            tops = self._rank(image_features, self.embeds[start:stop], top_count=keep_per_chunk)
-            top_labels.extend([self.labels[start+i] for i in tops])
-            top_embeds.extend([self.embeds[start+i] for i in tops])
-
-        tops = self._rank(image_features, top_embeds, top_count=top_count)
-        return [top_labels[i] for i in tops]
-
-def generate_caption(pil_image):
-    gpu_image = T.Compose([
-        T.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=TF.InterpolationMode.BICUBIC),
-        T.ToTensor(),
-        T.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
-    ])(pil_image).unsqueeze(0).to(device)
-
-    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_top(image_features, text_array):
-    text_tokens = clip.tokenize([text for text in text_array]).to(device)
-    with torch.no_grad():
-        text_features = clip_model.encode_text(text_tokens).float()
-    text_features /= text_features.norm(dim=-1, keepdim=True)
-
-    similarity = torch.zeros((1, len(text_array)), device=device)
-    for i in range(image_features.shape[0]):
-        similarity += (image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
-
-    _, top_labels = similarity.cpu().topk(1, dim=-1)
-    return text_array[top_labels[0][0].numpy()]
-
-def similarity(image_features, text):
-    text_tokens = clip.tokenize([text]).to(device)
-    with torch.no_grad():
-        text_features = clip_model.encode_text(text_tokens).float()       
-    text_features /= text_features.norm(dim=-1, keepdim=True)
-    similarity = text_features.cpu().numpy() @ image_features.cpu().numpy().T
-    return similarity[0][0]
-
-def interrogate(image):
-    caption = generate_caption(image)
-
-    images = clip_preprocess(image).unsqueeze(0).to(device)
-    with torch.no_grad():
-        image_features = clip_model.encode_image(images).float()
-    image_features /= image_features.norm(dim=-1, keepdim=True)
-
-    flaves = flavors.rank(image_features, flavor_intermediate_count)
-    best_medium = mediums.rank(image_features, 1)[0]
-    best_artist = artists.rank(image_features, 1)[0]
-    best_trending = trendings.rank(image_features, 1)[0]
-    best_movement = movements.rank(image_features, 1)[0]
-
-    best_prompt = caption
-    best_sim = similarity(image_features, best_prompt)
-
-    def check(addition):
-        nonlocal best_prompt, best_sim
-        prompt = best_prompt + ", " + addition
-        sim = similarity(image_features, prompt)
-        if sim > best_sim:
-            best_sim = sim
-            best_prompt = prompt
-            return True
-        return False
-
-    def check_multi_batch(opts):
-        nonlocal best_prompt, best_sim
-        prompts = []
-        for i in range(2**len(opts)):
-            prompt = best_prompt
-            for bit in range(len(opts)):
-                if i & (1 << bit):
-                    prompt += ", " + opts[bit]
-            prompts.append(prompt)
-
-        prompt = rank_top(image_features, prompts)
-        sim = similarity(image_features, prompt)
-        if sim > best_sim:
-            best_sim = sim
-            best_prompt = prompt
-
-    check_multi_batch([best_medium, best_artist, best_trending, best_movement])
-
-    extended_flavors = set(flaves)
-    for _ in tqdm(range(25), desc="Flavor chain"):
-        try:
-            best = rank_top(image_features, [f"{best_prompt}, {f}" for f in extended_flavors])
-            flave = best[len(best_prompt)+2:]
-            if not check(flave):
-                break
-            extended_flavors.remove(flave)
-        except:
-            # exceeded max prompt length
-            break
-
-    return best_prompt
-
-
-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])
-
-raw_artists = load_list('data/artists.txt')
-artists = [f"by {a}" for a in raw_artists]
-artists.extend([f"inspired by {a}" for a in raw_artists])
-
-artists = LabelTable(artists, "artists")
-flavors = LabelTable(load_list('data/flavors.txt'), "flavors")
-mediums = LabelTable(load_list('data/mediums.txt'), "mediums")
-movements = LabelTable(load_list('data/movements.txt'), "movements")
-trendings = LabelTable(trending_list, "trendings")
-
-
-def inference(image):
-    return interrogate(image)
-    
-inputs = [gr.inputs.Image(type='pil')]
-outputs = gr.outputs.Textbox(label="Output")
-
-title = "CLIP Interrogator"
-description = "Want to figure out what a good prompt might be to create new images like an existing one? The CLIP Interrogator is here to get you answers!"
-article = """
-<p>
-Example art by <a href="https://pixabay.com/illustrations/watercolour-painting-art-effect-4799014/">Layers</a> 
-and <a href="https://pixabay.com/illustrations/animal-painting-cat-feline-pet-7154059/">Lin Tong</a> 
-from pixabay.com
-</p>
 
-<p>
-Has this been helpful to you? Follow me on twitter 
-<a href="https://twitter.com/pharmapsychotic">@pharmapsychotic</a> 
-and check out more tools at my
-<a href="https://pharmapsychotic.com/tools.html">Ai generative art tools list</a>
-</p>
-"""
+def greet(name):
+    return "Hello " + name + "!!"
 
-gr.Interface(
-    inference, 
-    inputs, 
-    outputs, 
-    title=title, description=description, 
-    article=article, 
-    examples=[['example01.jpg'], ['example02.jpg']]
-).launch(enable_queue=True)
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()