diff --git a/.gitattributes b/.gitattributes
index 6ca683f916bae941fc277ba176c083a194480854..c7d9f3332a950355d5a77d85000f05e6f45435ea 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -32,11 +32,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text
-*.csv filter=lfs diff=lfs merge=lfs -text
-*.asset filter=lfs diff=lfs merge=lfs -text
-twitter.asset filter=lfs diff=lfs merge=lfs -text
-*.psd filter=lfs diff=lfs merge=lfs -text
-*.jpg filter=lfs diff=lfs merge=lfs -text
-*.jpeg filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
-*.svg filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
deleted file mode 100644
index 77cb0184dccdbb107b220dc0eff46fa792f3f59d..0000000000000000000000000000000000000000
--- a/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-/__pycache__
-*.pyc
diff --git a/app.py b/app.py
index ab000b397df0783cfc024f0eb2b1614e4ddfbce0..97b50710695e611aefd82d3d11d3de54e32f5df5 100644
--- a/app.py
+++ b/app.py
@@ -1,27 +1,30 @@
+import streamlit as st
+import pandas as pd
import home
+import numpy as np
+from PIL import Image
+import requests
+import transformers
import text2image
-import image2image
-import visualization
+import tokenizers
+from io import BytesIO
import streamlit as st
-import details
-
-st.set_page_config(layout="wide")
+from transformers import CLIPModel
+import clip
+import torch
+from transformers import (
+ VisionTextDualEncoderModel,
+ AutoFeatureExtractor,
+ AutoTokenizer
+)
+from transformers import AutoProcessor
-st.sidebar.title("WebPLIP")
-st.sidebar.markdown("## Menu")
+st.sidebar.title("Explore our PLIP Demo")
PAGES = {
"Introduction": home,
- "Details": details,
"Text to Image": text2image,
- "Image to Image": image2image,
- "Visualization": visualization,
}
page = st.sidebar.radio("", list(PAGES.keys()))
-st.sidebar.markdown("## Links")
-
-st.sidebar.markdown("[PLIP Model](https://huggingface.co/vinid/plip)")
-st.sidebar.markdown("[OpenPath Dataset](https://drive.google.com/drive/folders/1b5UT8BzUphkHZavRG-fmiyY9JWYIWZER)")
-st.sidebar.markdown("[PLIP Code](https://github.com/vinid/path_eval)")
PAGES[page].app()
diff --git a/data/.gitignore b/data/.gitignore
deleted file mode 100644
index 5ca97d6f6739719f810873d9d4180b7a65f8f80c..0000000000000000000000000000000000000000
--- a/data/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-/data_ignore
diff --git a/data/data_weblinks.csv b/data/data_weblinks.csv
deleted file mode 100644
index cf51b2eea901903390ccc20b2be28d47e187ebce..0000000000000000000000000000000000000000
--- a/data/data_weblinks.csv
+++ /dev/null
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--- a/data/example_images/Books_0fe87b11-b817-457d-9598-345a0479c11d.png
+++ /dev/null
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diff --git a/data/twitter.asset b/data/twitter.asset
deleted file mode 100644
index f1397b655b02b3d3df3096e881778655e9b8bc01..0000000000000000000000000000000000000000
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diff --git a/data/txt_2d_embedding.csv b/data/txt_2d_embedding.csv
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index 6b340d59314f722a18b1071bd0b7eff7bb23c6aa..0000000000000000000000000000000000000000
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diff --git a/details.py b/details.py
deleted file mode 100644
index d502bd24348e285b85b1850de2a0aa66097256e8..0000000000000000000000000000000000000000
--- a/details.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from pathlib import Path
-import streamlit as st
-import streamlit.components.v1 as components
-from PIL import Image
-import base64
-
-def read_markdown_file(markdown_file):
- return Path(markdown_file).read_text()
-
-def render_svg(svg_filename):
- with open(svg_filename,"r") as f:
- lines = f.readlines()
- svg=''.join(lines)
- """Renders the given svg string."""
- b64 = base64.b64encode(svg.encode('utf-8')).decode("utf-8")
- html = r'
' % b64
- st.write(html, unsafe_allow_html=True)
-
-
-def app():
- #intro_markdown = read_markdown_file("introduction.md")
- #st.markdown(intro_markdown, unsafe_allow_html=True)
- st.markdown("# Leveraging medical Twitter to build a visual-language foundation model for pathology")
-
-
- st.markdown("The lack of annotated publicly available medical images is a major barrier for innovations. At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter. Here we harness these crowd platforms to curate OpenPath, a large dataset of 208,414 pathology images paired with natural language descriptions. This is the largest public dataset for pathology images annotated with natural text. We demonstrate the value of this resource by developing PLIP, a multimodal AI with both image and text understanding, which is trained on OpenPath. PLIP achieves state-of-the-art zero-shot and few-short performance for classifying new pathology images across diverse tasks. Moreover, PLIP enables users to retrieve similar cases by either image or natural language search, greatly facilitating knowledge sharing. Our approach demonstrates that publicly shared medical data is a tremendous opportunity that can be harnessed to advance biomedical AI.", unsafe_allow_html=True)
- render_svg("resources/SVG/Asset 49.svg")
-
- st.markdown('#### Watch our successful image-to-image retrieval via PLIP:')
- col1, col2, col3, _, _ = st.columns([1, 1, 1, 1, 1])
- with col1:
- st.markdown("[Similar cells](https://twitter.com/ZhiHuangPhD/status/1641906064823312384)")
- example1 = Image.open('resources/example/1.png')
- st.image(example1, caption='Example 1', output_format='png')
- with col2:
- st.markdown("[Salient object](https://twitter.com/ZhiHuangPhD/status/1641899092195565569)")
- example2 = Image.open('resources/example/2.png')
- st.image(example2, caption='Example 2', output_format='png')
- with col3:
- st.markdown("[Similar region](https://twitter.com/ZhiHuangPhD/status/1641911235288645632)")
- example3 = Image.open('resources/example/3.png')
- st.image(example3, caption='Example 3', output_format='png')
-
-
- st.markdown("#### PLIP is trained on the largest public vision–language pathology dataset: OpenPath")
-
- col1, col2 = st.columns([1, 1])
- with col1:
- st.markdown("Following the usage policy and guidelines from Twitter and other entities, we established so far the largest public vision–language pathology dataset. To ensure the quality of the data, OpenPath followed rigorous protocols for cohort inclusion and exclusion, including the removal of retweets, sensitive tweets, and non-pathology images, as well as text cleaning.", unsafe_allow_html=True)
- st.markdown("The final OpenPath dataset consists of:", unsafe_allow_html=True)
- st.markdown("- Tweets: 116,504 image–text pairs from Twitter posts (tweets) during Mar. 21, 2006 – Nov. 15, 2022 across 32 pathology subspecialty-specific hashtags;", unsafe_allow_html=True)
- st.markdown("- Replies: 59,869 image–text pairs from the associated replies that received the highest number of likes in the tweet, if applicable;", unsafe_allow_html=True)
- st.markdown("- PathLAION: 32,041 additional image–text pairs from the Internet which are outside from the Twitter community extracted from the LAION dataset.", unsafe_allow_html=True)
- st.markdown("Leveraging the largest publicly available pathology dataset which contains image–text pairs across 32 different pathology subspecialty-specific hashtags, where each image has detailed text descriptions, we fine-tuned a pre-trained CLIP model and proposed a multimodal deep learning model for pathology, PLIP.", unsafe_allow_html=True)
- with col2:
- render_svg("resources/SVG/Asset 50.svg")
- render_svg("resources/SVG/Asset 51.svg")
-
-
-
- st.markdown("#### PLIP is trained with connecting the image and text via contrastive learning")
-
- col1, col2 = st.columns([3, 1])
- with col1:
- st.markdown("The proposed PLIP model generates two embedding vectors from both the text and image encoders. These vectors were then forced to be similar for each of the paired image and text vectors and dissimilar for non-paired image and text pairs via contrastive learning.", unsafe_allow_html=True)
- fig1e = Image.open('resources/4x/Fig1e.png')
- st.image(fig1e, caption='PLIP training', output_format='png')
-
- with col2:
- render_svg("resources/SVG/Asset 53.svg")
-
-
-
diff --git a/home.py b/home.py
index a2ed36cef04610fdcec8370d9d666c200c20c3a2..5def21699908bb7b546df59bc9d896ec6c5d961b 100644
--- a/home.py
+++ b/home.py
@@ -1,52 +1,11 @@
from pathlib import Path
import streamlit as st
-import streamlit.components.v1 as components
-from PIL import Image
-import base64
+
def read_markdown_file(markdown_file):
return Path(markdown_file).read_text()
-def render_svg(svg_filename):
- with open(svg_filename,"r") as f:
- lines = f.readlines()
- svg=''.join(lines)
- """Renders the given svg string."""
- b64 = base64.b64encode(svg.encode('utf-8')).decode("utf-8")
- html = r'' % b64
- st.write(html, unsafe_allow_html=True)
-
def app():
-
- st.markdown("# A visual-language foundation model for pathology")
- st.markdown("This is a webapp for PLIP, our new fundational AI model for pathology and OpenPath our new dataset, **from our recent work**: Leveraging medical Twitter to build a visual-language foundation model for pathology")
- st.markdown("### Pathology Language and Image Pretraining (PLIP)\n We develop PLIP, a multimodal AI with both image and text understanding. PLIP achieves state-of-the-art zero-shot and few-short performance for classifying new pathology images across diverse tasks. Moreover, PLIP enables users to retrieve similar cases by either image or natural language search, greatly facilitating knowledge sharing. Our approach demonstrates that publicly shared medical data is a tremendous opportunity that can be harnessed to advance biomedical AI.")
-
- fig1e = Image.open('resources/4x/Fig1e.png')
- st.image(fig1e, caption='PLIP training procedure', output_format='png')
-
- st.markdown("### OpenPath Dataset\nThe lack of annotated publicly available medical images is a major barrier for innovations. At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter. Here we harness these crowd platforms to curate OpenPath, a large dataset of **208,414** pathology images paired with natural language descriptions")
-
- render_svg("resources/SVG/Asset 49.svg")
-
-
- st.markdown("### Documentation\n"
- "This webapp comes with different functionalities.\n"
- "* Details: The details page guides you through our work.\n"
- "* Text to Image: allows users to perform text search on a database of images.\n"
- "* Image to Image: allows users to perform image search on a database of images.\n"
- "")
-
- st.markdown("### Other Links\n"
- "* Download [OpenPath](https://drive.google.com/drive/folders/1b5UT8BzUphkHZavRG-fmiyY9JWYIWZER)\n"
- "* Code to reproduce [PLIP](https://github.com/vinid/path_eval) results\n"
- "* Link to the [PLIP Model](https://huggingface.co/vinid/plip)\n"
- "")
-
- st.markdown("""---""")
- 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.')
-
- st.markdown('Privacy statement')
- st.caption('In accordance with the privacy and control policy of Twitter, we hereby declared that the data redistributed by us shall only comprise of Tweet IDs. The Tweet IDs will be employed to establish a linkage with the original Twitter post, as long as the original post is still accessible. The hyperlink will cease to function if the user deletes the original post. It is important to note that all tweets displayed on our service have already been classified as non-sensitive by Twitter. It is strictly prohibited to redistribute any content apart from the Tweet IDs. Any distribution carried out must adhere to the laws and regulations applicable in your jurisdiction, including export control laws and embargoes.')
+ intro_markdown = read_markdown_file("introduction.md")
+ st.markdown(intro_markdown, unsafe_allow_html=True)
\ No newline at end of file
diff --git a/image2image.py b/image2image.py
deleted file mode 100644
index ed99def32243e3def8254d3c9275d0f27a0959ed..0000000000000000000000000000000000000000
--- a/image2image.py
+++ /dev/null
@@ -1,239 +0,0 @@
-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('''
-
-
- ''' % target_weblinks[topn_value],
- height=600)
-
- 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('''
-
-
- ''' % target_weblinks[topn_value],
- height=800)
-
-
-
-
-
-
-
-
-
-
- st.markdown("""---""")
- 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.')
-
- st.markdown('Privacy statement')
- st.caption('In accordance with the privacy and control policy of Twitter, we hereby declared that the data redistributed by us shall only comprise of Tweet IDs. The Tweet IDs will be employed to establish a linkage with the original Twitter post, as long as the original post is still accessible. The hyperlink will cease to function if the user deletes the original post. It is important to note that all tweets displayed on our service have already been classified as non-sensitive by Twitter. It is strictly prohibited to redistribute any content apart from the Tweet IDs. Any distribution carried out must adhere to the laws and regulations applicable in your jurisdiction, including export control laws and embargoes.')
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/introduction.md b/introduction.md
new file mode 100644
index 0000000000000000000000000000000000000000..0cc29b419fadd12616b10d565177c2478e9595f1
--- /dev/null
+++ b/introduction.md
@@ -0,0 +1,2 @@
+
+# Welcome to our PLIP Demo
\ No newline at end of file
diff --git a/plip_support.py b/plip_support.py
new file mode 100644
index 0000000000000000000000000000000000000000..26352b8524b04e2603163a5ab922730dda441f72
--- /dev/null
+++ b/plip_support.py
@@ -0,0 +1,9 @@
+import clip
+import torch
+
+
+
+
+def embed_text(plip, text, device="cpu"):
+ idx = clip.tokenize([text], truncate=True).to(device)
+ return plip.encode_text(idx).detach().cpu().numpy()[0]
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index e1fd6d73ee85e4cd6925ced7e97222249dc537d0..f72b2c0510c3650ad8f01f5b317c38f943f943a6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,7 @@
-
-tokenizers
-pandas
+git+https://github.com/openai/CLIP.git
torch
transformers
-st_clickable_images
-plotly
-altair<5.0
\ No newline at end of file
+pandas
+numpy
+Pillow
+streamlit==1.19.0
\ No newline at end of file
diff --git a/resources/4x/Fig1.png b/resources/4x/Fig1.png
deleted file mode 100644
index 966c93d310e04cf15cf9d3feebea970e4057c900..0000000000000000000000000000000000000000
--- a/resources/4x/Fig1.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:84a7d43d4ebef1a392eac1307a6c331488d2f572dee588202868209a511bfe2f
-size 6712026
diff --git a/resources/4x/Fig1ab.png b/resources/4x/Fig1ab.png
deleted file mode 100644
index a2b2818e87d962636c6172a57495ed062522e6bc..0000000000000000000000000000000000000000
--- a/resources/4x/Fig1ab.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8dc787aaa8cb2b331d20773887f28a4e3563dc26b65da07d77960b9858dab402
-size 2733565
diff --git a/resources/4x/Fig1c.png b/resources/4x/Fig1c.png
deleted file mode 100644
index 676f0cb58ea3c70b8ee47d52b0a49ab99192e930..0000000000000000000000000000000000000000
--- a/resources/4x/Fig1c.png
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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-size 264514
diff --git a/resources/4x/Fig1d.png b/resources/4x/Fig1d.png
deleted file mode 100644
index 5d71bec7d4d1617559aff96d6f6e3df9cd84d41a..0000000000000000000000000000000000000000
--- a/resources/4x/Fig1d.png
+++ /dev/null
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-size 141021
diff --git a/resources/4x/Fig1e.png b/resources/4x/Fig1e.png
deleted file mode 100644
index b7c29ec17fd0c4bc75132ed764730abad81f6703..0000000000000000000000000000000000000000
--- a/resources/4x/Fig1e.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/resources/4x/Fig1f.png b/resources/4x/Fig1f.png
deleted file mode 100644
index b40c46cebadbde6228d64682ccadce256552b6c3..0000000000000000000000000000000000000000
--- a/resources/4x/Fig1f.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:67140f0a485afe810ef7eba2c0412b9a46dbc796f26aa4c014076323507db4db
-size 134368
diff --git a/resources/4x/image_retrieval.png b/resources/4x/image_retrieval.png
deleted file mode 100644
index 51c634f6f5dc4de55b3329fcc95db14a20338f1b..0000000000000000000000000000000000000000
--- a/resources/4x/image_retrieval.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:688c3eb015a8f8a80e2fe88cd8a73abda0e48e6c11756435702075e9679df0c0
-size 618727
diff --git a/resources/SVG/.DS_Store b/resources/SVG/.DS_Store
deleted file mode 100644
index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000
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deleted file mode 100644
index fa0346b8025d188d75b409b488f64f1d61db2c75..0000000000000000000000000000000000000000
--- a/resources/SVG/Asset 47.svg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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deleted file mode 100644
index 006425e9d67e92632708f2462a0fab807ee0e375..0000000000000000000000000000000000000000
--- a/resources/SVG/Asset 48.svg
+++ /dev/null
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deleted file mode 100644
index dfac137d72397a71b6718300706b16bcb3e2d407..0000000000000000000000000000000000000000
--- a/resources/SVG/Asset 49.svg
+++ /dev/null
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deleted file mode 100644
index b15ff2476e39aea111524ec5d3d09f4fc9bc1adc..0000000000000000000000000000000000000000
--- a/resources/SVG/Asset 50.svg
+++ /dev/null
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deleted file mode 100644
index 9073893891faa5adb21bd893f0fddc366d56979e..0000000000000000000000000000000000000000
--- a/resources/SVG/Asset 51.svg
+++ /dev/null
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deleted file mode 100644
index f7d2a640bc107717aba2aa42634a0ed71ac4576e..0000000000000000000000000000000000000000
--- a/resources/SVG/Asset 52.svg
+++ /dev/null
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deleted file mode 100644
index 2735cc4974501cc0110385cee52d2f937c97f088..0000000000000000000000000000000000000000
--- a/resources/SVG/Asset 53.svg
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deleted file mode 100644
index 506c55d46676f5fd8009db228e958ce7588e444f..0000000000000000000000000000000000000000
--- a/resources/SVG/Asset 54.svg
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diff --git a/resources/example/1.png b/resources/example/1.png
deleted file mode 100644
index 60de59a8a5fb42a42252d2232ba6f89c9c286874..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
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diff --git a/text2image.py b/text2image.py
index 1c9b15f891ff4e25ce01fac9cc4b997974aea3b9..282dd582304ff162e18dbbaca7710da11a63496d 100644
--- a/text2image.py
+++ b/text2image.py
@@ -1,58 +1,23 @@
import streamlit as st
import pandas as pd
+from plip_support import embed_text
import numpy as np
from PIL import Image
-import pickle
+import requests
+import transformers
import tokenizers
+from io import BytesIO
+import streamlit as st
+from transformers import CLIPModel
+import clip
import torch
-
from transformers import (
-
- CLIPModel,
- AutoProcessor
+ VisionTextDualEncoderModel,
+ AutoFeatureExtractor,
+ AutoTokenizer
)
-import streamlit.components.v1 as components
-import base64
-
-def render_svg(svg_filename):
- with open(svg_filename,"r") as f:
- lines = f.readlines()
- svg=''.join(lines)
- """Renders the given svg string."""
- b64 = base64.b64encode(svg.encode('utf-8')).decode("utf-8")
- html = r'' % b64
- st.write(html, unsafe_allow_html=True)
-
-@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
+from transformers import AutoProcessor
-@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")
@@ -65,162 +30,45 @@ def embed_texts(model, texts, processor):
)
return embeddings
+@st.cache
+def load_embeddings(embeddings_path):
+ print("loading embeddings")
+ return np.load(embeddings_path)
-def app():
-
- st.title('Text to Image Retrieval')
- st.markdown('#### A pathology image search engine that geos from texts to images.')
-
- col1, col2 = st.columns([1,1])
- with col1:
- st.markdown("The text-to-image retrieval system can serve as an image search engine, enabling users to match images from multiple queries and retrieve the most relevant image based on a sentence description. This generic system can comprehend semantic and interrelated knowledge, such as “Breast tumor surrounded by fat”.")
- st.markdown("Unlike searching keywords and sentences from Google and indirectly matching the images from the target text, our proposed pathology image retrieval allows direct comparison between input sentences and images.")
- with col2:
- render_svg("resources/SVG/Asset 54.svg")
-
- meta, image_embedding, text_embedding, validation_subset_index = init()
- model, processor = load_path_clip()
-
- st.markdown('### Search')
- st.markdown('How to use this: first of all, select a dataset on which to do retrieval.\n'
- 'Then, either select a predefined search query or input one yourself.')
-
-
- col1, col2 = st.columns(2)
- with col1:
- data_options = ["All Twitter Data (03/21/2006 — 01/15/2023)",
- "Validation Twitter data (11/16/2022 — 01/15/2023)"]
- st.selectbox(
- "Dataset",
- 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,
- )
-
- col1, col2 = st.columns(2)
-
- with col1:
- # Create selectbox
- examples = ['Breast tumor surrounded by fat',
- 'HER2+ breast tumor',
- 'Colorectal cancer tumor on epithelium',
- 'An image of endometrium epithelium',
- 'Breast cancer DCIS',
- 'Papillary carcinoma in breast tissue',
- ]
- query_1 = st.selectbox("Select an example", options=examples)
-
- col1_submit = True
-
- with col2:
- form = st.form(key='my_form')
- query_2 = form.text_input(label='Or input your custom query:')
- submit_button = form.form_submit_button(label='Submit')
-
- if submit_button:
- col1_submit = False
-
- if col1_submit:
- query = query_1
- else:
- query = query_2
-
- input_text = embed_texts(model, [query], processor)[0].detach().cpu().numpy()
- input_text = input_text/np.linalg.norm(input_text)
-
- # Sort IDs by cosine-similarity from high to low
-
- if st.session_state.calculation_option == retrieval_options[0]: # Image only
- similarity_scores = input_text.dot(image_embedding.T)
- else: # Text and Image
- similarity_scores_i = input_text.dot(image_embedding.T)
- similarity_scores_t = input_text.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
- ############################################################
-
- text = 'Your input query: %s' % query + \
- ' (Try search it directly on [Twitter](https://twitter.com/search?q=%s&src=typed_query) or [Google](https://www.google.com/search?q=%s))' % (query.replace(' ', '%20'), query.replace(' ', '+'))
- st.markdown(text, unsafe_allow_html=True)
-
- 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('''
-
-
- ''' % target_weblinks[topn_value],
- height=600)
-
- 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('''
-
-
- ''' % target_weblinks[topn_value],
- height=800)
-
-
-
- st.markdown("""---""")
- 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.')
-
- st.markdown('Privacy statement')
- st.caption('In accordance with the privacy and control policy of Twitter, we hereby declared that the data redistributed by us shall only comprise of Tweet IDs. The Tweet IDs will be employed to establish a linkage with the original Twitter post, as long as the original post is still accessible. The hyperlink will cease to function if the user deletes the original post. It is important to note that all tweets displayed on our service have already been classified as non-sensitive by Twitter. It is strictly prohibited to redistribute any content apart from the Tweet IDs. Any distribution carried out must adhere to the laws and regulations applicable in your jurisdiction, including export control laws and embargoes.')
-
+@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
+def app():
+ st.title('PLIP Image Search')
+ plip_dataset = pd.read_csv("tweet_eval_retrieval.tsv", sep="\t")
+ model, processor = load_path_clip()
+ image_embedding = load_embeddings("tweet_eval_embeddings.npy")
+ query = st.text_input('Search Query', '')
+ if query:
+ text_embedding = embed_texts(model, [query], processor)[0].detach().cpu().numpy()
+ text_embedding = text_embedding/np.linalg.norm(text_embedding)
+ best_id = np.argmax(text_embedding.dot(image_embedding.T))
+ url = (plip_dataset.iloc[best_id]["imageURL"])
+ response = requests.get(url)
+ img = Image.open(BytesIO(response.content))
+ st.image(img)
\ No newline at end of file
diff --git a/tweet_eval_embeddings.npy b/tweet_eval_embeddings.npy
new file mode 100644
index 0000000000000000000000000000000000000000..edbddb2b371fc5848279576b08e2da46de365d93
--- /dev/null
+++ b/tweet_eval_embeddings.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36e445b069b1d937a0a780ddeab9239df5fd13264e8cd1f6cf033be3210352e1
+size 2401408
diff --git a/tweet_eval_retrieval.tsv b/tweet_eval_retrieval.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..00324b5230319773c16b12e7bb7ab8cb0194ea2d
--- /dev/null
+++ b/tweet_eval_retrieval.tsv
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diff --git a/visualization.py b/visualization.py
deleted file mode 100644
index 563f8868747d48b7af05417ffca00c433006c7d4..0000000000000000000000000000000000000000
--- a/visualization.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from pathlib import Path
-import streamlit as st
-import streamlit.components.v1 as components
-import plotly.figure_factory as ff
-import numpy as np
-import pandas as pd
-#from streamlit_plotly_events import plotly_events
-
-
-
-def app():
- st.markdown('#### Visualization')
-
- img_2d_embed = pd.read_csv('data/img_2d_embedding.csv', index_col=0)
- img_2d_embed = img_2d_embed.sample(frac=0.1, random_state=0)
-
- txt_2d_embed = pd.read_csv('data/txt_2d_embedding.csv', index_col=0)
- txt_2d_embed = txt_2d_embed.sample(frac=0.1, random_state=0)
-
- col1, col2 = st.columns(2)
- with col1:
- fig1 = ff.create_2d_density(
- x=img_2d_embed['UMAP_1'],
- y=img_2d_embed['UMAP_2'],
- #colors=img_2d_embed['tag'],
- colorscale='Blues', # set the color map
- height=500, # set height of the figure
- width=500, # set width of the figure
- title='Image embedding visualized in 2D UMAP'
- )
- #selected_points = plotly_events(fig1, click_event=True, hover_event=True)
- st.plotly_chart(fig1, use_container_width=True)
-
- with col2:
- fig2 = ff.create_2d_density(
- x=txt_2d_embed['UMAP_1'],
- y=txt_2d_embed['UMAP_2'],
- #colors=img_2d_embed['tag'],
- colorscale='Blues', # set the color map
- height=500, # set height of the figure
- width=500, # set width of the figure
- title='Text embedding visualized in 2D UMAP'
- )
- st.plotly_chart(fig2, use_container_width=True)
\ No newline at end of file
diff --git a/viz_scripts/calc_img.py b/viz_scripts/calc_img.py
deleted file mode 100644
index 37b4ab30e66aac26435734b1db4b12e8c2371066..0000000000000000000000000000000000000000
--- a/viz_scripts/calc_img.py
+++ /dev/null
@@ -1,75 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Mar 10 21:13:04 2023
-
-@author: zhihuang
-"""
-
-import pickle
-import os
-import pandas as pd
-import numpy as np
-import umap
-import seaborn as sns
-import matplotlib.pyplot as plt
-opj=os.path.join
-
-if __name__ == '__main__':
- dd = '/home/zhihuang/Desktop/webplip/data'
- with open(opj(dd, 'twitter.asset'),'rb') as f:
- data = pickle.load(f)
-
- n_neighbors = 15
- random_state = 0
-
- reducer = umap.UMAP(n_components=2,
- n_neighbors=n_neighbors,
- min_dist=0.1,
- metric='euclidean',
- random_state=random_state)
- img_2d = reducer.fit(data['image_embedding'])
- img_2d = reducer.transform(data['image_embedding'])
- df_img = pd.DataFrame(np.c_[img_2d, data['meta'].values], columns = ['UMAP_1','UMAP_2'] + list(data['meta'].columns))
- df_img.to_csv(opj(dd, 'img_2d_embedding.csv'))
-
-
- # reducer = umap.UMAP(n_components=2,
- # n_neighbors=n_neighbors,
- # min_dist=0.1,
- # metric='euclidean',
- # random_state=random_state)
- txt_2d = reducer.fit_transform(data['text_embedding'])
- df_txt = pd.DataFrame(np.c_[txt_2d, data['meta'].values], columns = ['UMAP_1','UMAP_2'] + list(data['meta'].columns))
- df_txt.to_csv(opj(dd, 'txt_2d_embedding.csv'))
-
-
-
-
- fig, ax = plt.subplots(1,2, figsize=(20,10))
- sns.scatterplot(data=df_img,
- x='UMAP_1',
- y='UMAP_2',
- alpha=0.2,
- ax=ax[0],
- hue='tag'
- )
-
- sns.scatterplot(data=df_txt,
- x='UMAP_1',
- y='UMAP_2',
- alpha=0.2,
- ax=ax[1],
- hue='tag'
- )
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file