app.py

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] + ".jpg"
    with open(local_path, 'wb') as handler:
        handler.write(img_data)
    return local_path
    
def predict(query):
    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 = values[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]
    paths = [download_img(df.iloc[i]['path']) for i in results]
    print(paths)
    return {LABELS[i]: v.item() for i, v in zip(indices, values)}, paths

title = "Draw to Search"
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,
)
iface.launch(debug=True)