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| import os | |
| from pathlib import Path | |
| import pandas as pd, numpy as np | |
| from transformers import CLIPProcessor, CLIPTextModel, CLIPModel | |
| import torch | |
| from torch import nn | |
| import gradio as gr | |
| import requests | |
| LABELS = Path('class_names.txt').read_text().splitlines() | |
| class_model = nn.Sequential( | |
| nn.Conv2d(1, 32, 3, padding='same'), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(32, 64, 3, padding='same'), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, 3, padding='same'), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Flatten(), | |
| nn.Linear(1152, 256), | |
| nn.ReLU(), | |
| nn.Linear(256, len(LABELS)), | |
| ) | |
| state_dict = torch.load('pytorch_model.bin', map_location='cpu') | |
| class_model.load_state_dict(state_dict, strict=False) | |
| class_model.eval() | |
| model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") | |
| processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") | |
| df = pd.read_csv('data2.csv') | |
| embeddings_npy = np.load('embeddings.npy') | |
| embeddings = np.divide(embeddings_npy, np.sqrt(np.sum(embeddings_npy**2, axis=1, keepdims=True))) | |
| def compute_text_embeddings(list_of_strings): | |
| inputs = processor(text=list_of_strings, return_tensors="pt", padding=True) | |
| return model.get_text_features(**inputs) | |
| def download_img(path): | |
| img_data = requests.get(path).content | |
| local_path = path.split("/")[-1] | |
| with open(local_path, 'wb') as handler: | |
| handler.write(img_data) | |
| return local_path | |
| def predict(im): | |
| x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255. | |
| with torch.no_grad(): | |
| out = class_model(x) | |
| probabilities = torch.nn.functional.softmax(out[0], dim=0) | |
| values, indices = torch.topk(probabilities, 5) | |
| query = LABELS[indices[0]] | |
| n_results=3 | |
| text_embeddings = compute_text_embeddings([query]).detach().numpy() | |
| results = np.argsort((embeddings@text_embeddings.T)[:, 0])[-1:-n_results-1:-1] | |
| outputs = [download_img(df.iloc[i]['path']) for i in results] | |
| outputs.insert(0, {LABELS[i]: v.item() for i, v in zip(indices, values)}) | |
| print(outputs) | |
| return outputs | |
| title = "Draw to Search" | |
| description = "Using the power of CLIP and a simple small CNN, find images from movies based on what you draw!" | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs='sketchpad', | |
| outputs=[gr.outputs.Label(num_top_classes=3), gr.outputs.Image(type="file"), gr.outputs.Image(type="file"), gr.outputs.Image(type="file")], | |
| title=title, | |
| description=description, | |
| live=True | |
| ) | |
| iface.launch(debug=True) | |