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| import pandas as pd, numpy as np | |
| import os | |
| from transformers import CLIPProcessor, CLIPTextModel, CLIPModel | |
| import gradio as gr | |
| import requests | |
| 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] + ".jpg" | |
| with open(local_path, 'wb') as handler: | |
| handler.write(img_data) | |
| return local_path | |
| def predict(query): | |
| n_results=3 | |
| text_embeddings = compute_text_embeddings([query]).detach().numpy() | |
| results = np.argsort((embeddings@text_embeddings.T)[:, 0])[-1:-n_results-1:-1] | |
| paths = [download_img(df.iloc[i]['path']) for i in results] | |
| print(paths) | |
| return paths | |
| title = "Draw to Search" | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=[gr.inputs.Textbox(label="text", lines=3)], | |
| outputs=[gr.outputs.Image(type="file"), gr.outputs.Image(type="file"), gr.outputs.Image(type="file")], | |
| title=title, | |
| examples=[["Sunset"]] | |
| ) | |
| iface.launch(debug=True) | |