app.py

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)