image2image.py

import streamlit as st
import pandas as pd
import numpy as np
from PIL import Image
import requests
import tokenizers
import os
from io import BytesIO
import pickle
import base64
import datetime

import torch
from transformers import (
    VisionTextDualEncoderModel,
    AutoFeatureExtractor,
    AutoTokenizer,
    CLIPModel,
    AutoProcessor
)
import streamlit.components.v1 as components
from st_clickable_images import clickable_images #pip install st-clickable-images


@st.cache(
    hash_funcs={
        torch.nn.parameter.Parameter: lambda _: None,
        tokenizers.Tokenizer: lambda _: None,
        tokenizers.AddedToken: lambda _: None
    }
)
def load_path_clip():
    model = CLIPModel.from_pretrained("vinid/plip")
    processor = AutoProcessor.from_pretrained("vinid/plip")
    return model, processor

@st.cache
def init():
    with open('data/twitter.asset', 'rb') as f:
        data = pickle.load(f)
    meta = data['meta'].reset_index(drop=True)
    image_embedding = data['image_embedding']
    text_embedding = data['text_embedding']
    print(meta.shape, image_embedding.shape)
    validation_subset_index = meta['source'].values == 'Val_Tweets'
    return meta, image_embedding, text_embedding, validation_subset_index

def embed_images(model, images, processor):
    inputs = processor(images=images)
    pixel_values = torch.tensor(np.array(inputs["pixel_values"]))

    with torch.no_grad():
        embeddings = model.get_image_features(pixel_values=pixel_values)
    return embeddings

def embed_texts(model, texts, processor):
    inputs = processor(text=texts, padding="longest")
    input_ids = torch.tensor(inputs["input_ids"])
    attention_mask = torch.tensor(inputs["attention_mask"])

    with torch.no_grad():
        embeddings = model.get_text_features(
            input_ids=input_ids, attention_mask=attention_mask
        )
    return embeddings
def app():
    st.title('Image to Image Retrieval')
    st.markdown('#### A pathology image search engine that correlate images with images.')
    st.markdown("Image-to-image retrieval can be used to retrieve pathology images that have contents similar to the target image input, with the ability to comprehend the key components from the input image.")
    
    st.markdown('#### Demo')
    
    meta, image_embedding, text_embedding, validation_subset_index = init()
    model, processor = load_path_clip()


    col1, col2 = st.columns(2)
    with col1:
        data_options = ["All twitter data (03/21/2006 — 01/15/2023)",
                        "Twitter validation data (11/16/2022 — 01/15/2023)"]
        st.radio(
            "Choose dataset for image retrieval 👉",
            key="datapool",
            options=data_options,
        )
    with col2:
        retrieval_options = ["Image only",
                            "Text and image (beta)",
                             ]
        st.radio(
            "Similarity calcuation 👉",
            key="calculation_option",
            options=retrieval_options,
        )


    st.markdown('Try out following examples:')
    example_path = 'data/example_images'
    list_of_examples = [os.path.join(example_path, v) for v in os.listdir(example_path)]
    example_imgs = []
    for file in list_of_examples:
        with open(file, "rb") as image:
            encoded = base64.b64encode(image.read()).decode()
            example_imgs.append(f"data:image/jpeg;base64,{encoded}")
    clicked = clickable_images(
        example_imgs,
        titles=[f"Image #{str(i)}" for i in range(len(example_imgs))],
        div_style={"display": "flex", "justify-content": "center", "flex-wrap": "wrap"},
        img_style={"margin": "5px", "height": "70px"},
    )
    isExampleClicked = False
    if clicked > -1:
        image = Image.open(list_of_examples[clicked])
        isExampleClicked = True
        





    col1, col2, _ = st.columns(3)
    with col1:
        query = st.file_uploader("Choose a file to upload")


    proceed = False
    if query:
        image = Image.open(query)
        proceed = True
    elif isExampleClicked:
        proceed = True

    if proceed:
        with col2:
            st.image(image, caption='Your upload')

        input_image = embed_images(model, [image], processor)[0].detach().cpu().numpy()

        input_image = input_image/np.linalg.norm(input_image)
        
        # Sort IDs by cosine-similarity from high to low

        if st.session_state.calculation_option == retrieval_options[0]: # Image only
            similarity_scores = input_image.dot(image_embedding.T)
        else: # Text and Image
            similarity_scores_i = input_image.dot(image_embedding.T)
            similarity_scores_t = input_image.dot(text_embedding.T)
            similarity_scores_i = similarity_scores_i/np.max(similarity_scores_i)
            similarity_scores_t = similarity_scores_t/np.max(similarity_scores_t)
            similarity_scores = (similarity_scores_i + similarity_scores_t)/2


        ############################################################
        # Get top results
        ############################################################
        topn = 5
        df = pd.DataFrame(np.c_[np.arange(len(meta)), similarity_scores, meta['weblink'].values], columns = ['idx', 'score', 'twitterlink'])
        if st.session_state.datapool == data_options[1]: #Use val twitter data
            df = df.loc[validation_subset_index,:]
        df = df.sort_values('score', ascending=False)
        df = df.drop_duplicates(subset=['twitterlink'])
        best_id_topk = df['idx'].values[:topn]
        target_scores = df['score'].values[:topn]
        target_weblinks = df['twitterlink'].values[:topn]



        ############################################################
        # Display results
        ############################################################
        
        st.markdown('#### Top 5 results:')
        topk_options = ['1st', '2nd', '3rd', '4th', '5th']
        tab = {}
        tab[0], tab[1], tab[2] = st.columns(3)
        for i in [0,1,2]:
            with tab[i]:
                topn_value = i
                topn_txt = topk_options[i]
                st.caption(f'The {topn_txt} relevant image (similarity = {target_scores[topn_value]:.4f})')
                components.html('''
                    <blockquote class="twitter-tweet">
                        <a href="%s"></a>
                    </blockquote>
                    <script async src="https://platform.twitter.com/widgets.js" charset="utf-8">
                    </script>
                    ''' % target_weblinks[topn_value],
                height=800)

        tab[3], tab[4], tab[5] = st.columns(3)
        for i in [3,4]:
            with tab[i]:
                topn_value = i
                topn_txt = topk_options[i]
                st.caption(f'The {topn_txt} relevant image (similarity = {target_scores[topn_value]:.4f})')
                components.html('''
                    <blockquote class="twitter-tweet">
                        <a href="%s"></a>
                    </blockquote>
                    <script async src="https://platform.twitter.com/widgets.js" charset="utf-8">
                    </script>
                    ''' % target_weblinks[topn_value],
                height=800)










    st.markdown('Disclaimer')
    st.caption('Please be advised that this function has been developed in compliance with the Twitter policy of data usage and sharing. It is important to note that the results obtained from this function are not intended to constitute medical advice or replace consultation with a qualified medical professional. The use of this function is solely at your own risk and should be consistent with applicable laws, regulations, and ethical considerations. We do not warrant or guarantee the accuracy, completeness, suitability, or usefulness of this function for any particular purpose, and we hereby disclaim any liability arising from any reliance placed on this function or any results obtained from its use. If you wish to review the original Twitter post, you should access the source page directly on Twitter.')