app.py

import gradio as gr
import os
import numpy as np
import pandas as pd
from IPython import display
import faiss
import torch
from transformers import AutoTokenizer, CLIPTextModelWithProjection


TITLE="""<h1 style="font-size: 42px;" align="center">Video Retrieval</h1>"""

DESCRIPTION="""This is a video retrieval demo using [Diangle/clip4clip-webvid](https://huggingface.co/Diangle/clip4clip-webvid)."""
IMAGE='<div style="text-align: left;"><img src="https://huggingface.co/spaces/Diangle/Clip4Clip-webvid/resolve/main/Searchium.png" width="333" height="216"/>'

DATA_PATH = './data'

ft_visual_features_file = DATA_PATH + '/dataset_v1_visual_features_database.npy'

#load database features:
ft_visual_features_database = np.load(ft_visual_features_file)

database_csv_path = os.path.join(DATA_PATH, 'dataset_v1.csv')
database_df = pd.read_csv(database_csv_path)


class NearestNeighbors:
    """
    Class for NearestNeighbors.   
    """
    def __init__(self, n_neighbors=10, metric='cosine', rerank_from=-1):
        """
         metric = 'cosine' / 'binary' 
         if metric ~= 'cosine' and rerank_from > n_neighbors then a cosine rerank will be performed
        """
        self.n_neighbors = n_neighbors
        self.metric = metric        
        self.rerank_from = rerank_from                
        
    def normalize(self, a):
        return a / np.sum(a**2, axis=1, keepdims=True)
    
    def fit(self, data, o_data=None):
        if self.metric == 'cosine':
            data = self.normalize(data)
            self.index = faiss.IndexFlatIP(data.shape[1])     
        elif self.metric == 'binary':
            self.o_data = data if o_data is None else o_data
            #assuming data already packed
            self.index = faiss.IndexBinaryFlat(data.shape[1]*8)            
        self.index.add(np.ascontiguousarray(data))
        
    def kneighbors(self, q_data):                
        if self.metric == 'cosine':
            q_data = self.normalize(q_data)      
            sim, idx = self.index.search(q_data, self.n_neighbors)        
        else:            
            if self.metric == 'binary':
                print('binary search: ')
                bq_data = np.packbits((q_data > 0.0).astype(bool), axis=1)            
            print(bq_data.shape, self.index.d)
            sim, idx = self.index.search(bq_data, max(self.rerank_from, self.n_neighbors))
            
            if self.rerank_from > self.n_neighbors:
                rerank_data = self.o_data[idx[0]]
                rerank_search = NearestNeighbors(n_neighbors=self.n_neighbors, metric='cosine') 
                rerank_search.fit(rerank_data)
                sim, re_idxs = rerank_search.kneighbors(q_data)
                idx = [idx[0][re_idxs[0]]]
        
        return sim, idx
    
model = CLIPTextModelWithProjection.from_pretrained("Diangle/clip4clip-webvid")
tokenizer = AutoTokenizer.from_pretrained("Diangle/clip4clip-webvid")
    
def search(search_sentence):
    inputs = tokenizer(text=search_sentence , return_tensors="pt", padding=True)

    outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], return_dict=False)
    # Customized projection layer    
    text_projection = model.state_dict()['text_projection.weight']
    text_embeds = outputs[1] @ text_projection
    final_output = text_embeds[torch.arange(text_embeds.shape[0]), inputs["input_ids"].argmax(dim=-1)]

    # Normalization
    final_output = final_output / final_output.norm(dim=-1, keepdim=True)
    final_output = final_output.cpu().detach().numpy()
    sequence_output = final_output / np.sum(final_output**2, axis=1, keepdims=True)
    
    nn_search = NearestNeighbors(n_neighbors=5, metric='binary', rerank_from=100)
    nn_search.fit(np.packbits((ft_visual_features_database > 0.0).astype(bool), axis=1), o_data=ft_visual_features_database)
    sims, idxs = nn_search.kneighbors(sequence_output)  
    return database_df.iloc[idxs[0]]['contentUrl'].to_list()


with gr.Blocks() as demo:
    gr.HTML(TITLE)
    gr.Markdown(DESCRIPTION)
    gr.HTML(IMAGE)
    gr.Markdown("Retrieval of top 5 videos relevant to the input sentence: ")
    with gr.Row():
        with gr.Column():
            inp = gr.Textbox(placeholder="Write a sentence.")
            btn = gr.Button(value="Retrieve")
            ex = [["a woman waving to the camera"],["a basketball player performing a slam dunk"], ["how to bake a chocolate cake"], ["birds fly in the sky"]]
            gr.Examples(examples=ex,
                    inputs=[inp],
                    )
        with gr.Column():
            out = [gr.Video(format='mp4') for _ in range(5)]
        btn.click(search, inputs=inp, outputs=out)
        
demo.launch()