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app.py
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app.py
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import random
import requests
import streamlit as st
from clip_model import ClipModel
from PIL import Image
IMAGES_LINKS = ["https://cdn.pixabay.com/photo/2014/10/13/21/34/clipper-487503_960_720.jpg",
"https://cdn.pixabay.com/photo/2019/09/06/04/25/beach-4455433_960_720.jpg",
"https://cdn.pixabay.com/photo/2019/11/11/14/30/zebra-4618513_960_720.jpg",
"https://cdn.pixabay.com/photo/2020/11/04/15/29/coffee-beans-5712780_960_720.jpg",
"https://cdn.pixabay.com/photo/2020/03/24/20/42/namibia-4965457_960_720.jpg",
"https://cdn.pixabay.com/photo/2020/08/27/07/31/restaurant-5521372_960_720.jpg",
"https://cdn.pixabay.com/photo/2020/08/24/21/41/couple-5515141_960_720.jpg",
"https://cdn.pixabay.com/photo/2020/01/31/07/10/billboards-4807268_960_720.jpg",
"https://cdn.pixabay.com/photo/2017/07/31/20/48/shell-2560930_960_720.jpg",
"https://cdn.pixabay.com/photo/2020/08/13/01/29/koala-5483931_960_720.jpg",
]
@st.cache # Cache this so that it doesn't change every time something changes in the page
def load_default_dataset():
return [load_image_from_url(url) for url in IMAGES_LINKS]
def load_image_from_url(url: str) -> Image.Image:
return Image.open(requests.get(url, stream=True).raw)
@st.cache
def load_model(model_architecture: str) -> ClipModel:
return ClipModel(model_architecture)
def init_state():
if "images" not in st.session_state:
st.session_state.images = None
if "prompts" not in st.session_state:
st.session_state.prompts = None
if "predictions" not in st.session_state:
st.session_state.predictions = None
if "default_text_input" not in st.session_state:
st.session_state.default_text_input = None
if "model_architecture" not in st.session_state:
st.session_state.model_architecture = "RN50"
def limit_number_images():
"""When moving between tasks sometimes the state of images can have too many samples"""
if st.session_state.images is not None and len(st.session_state.images) > 1:
st.session_state.images = [st.session_state.images[0]]
def limit_number_prompts():
"""When moving between tasks sometimes the state of prompts can have too many samples"""
if st.session_state.prompts is not None and len(st.session_state.prompts) > 1:
st.session_state.prompts = [st.session_state.prompts[0]]
def is_valid_prediction_state() -> bool:
if st.session_state.images is None or len(st.session_state.images) < 1:
st.error("Choose at least one image before predicting")
return False
if st.session_state.prompts is None or len(st.session_state.prompts) < 1:
st.error("Write at least one prompt before predicting")
return False
return True
def preprocess_image(image: Image.Image, max_size: int = 1200) -> Image.Image:
"""Set up a max size because otherwise the API sometimes breaks"""
width_0, height_0 = image.size
if max((width_0, height_0)) <= max_size:
return image
if width_0 > height_0:
aspect_ratio = max_size / float(width_0)
new_height = int(float(height_0) * float(aspect_ratio))
image = image.resize((max_size, new_height), Image.ANTIALIAS)
return image
else:
aspect_ratio = max_size / float(height_0)
new_width = int(float(width_0) * float(aspect_ratio))
image = image.resize((max_size, new_width), Image.ANTIALIAS)
return image
class Sections:
@staticmethod
def header():
st.markdown('<link rel="stylesheet" '
'href="https://fonts.googleapis.com/css?family=Merriweather+Sans">'
'<style> '
'h1 {font-family: "Merriweather Sans", sans-serif; font-size: 48px; color: #f57c70}'
'a {color: #e6746a !important}'
'.stButton>button {'
' color: white;'
' background: #e6746a;'
' display:inline-block;'
' width: 100%;'
' border-width: 0px;'
' font-weight: 500;'
' padding-top: 10px;'
' padding-bottom: 10px;'
'}'
'</style>', unsafe_allow_html=True)
st.markdown("# CLIP Playground")
st.markdown("### Try OpenAI's CLIP model in your browser")
st.markdown(" ")
st.markdown(" ")
with st.expander("What is CLIP?"):
st.markdown("CLIP is a machine learning model that computes similarity between text "
"(also called prompts) and images. It has been trained on a dataset with millions of diverse"
" image-prompt pairs, which allows it to generalize to unseen examples."
" <br /> Check out [OpenAI's blogpost](https://openai.com/blog/clip/) for more details",
unsafe_allow_html=True)
col1, col2 = st.columns(2)
col1.image("https://openaiassets.blob.core.windows.net/$web/clip/draft/20210104b/overview-a.svg")
col2.image("https://openaiassets.blob.core.windows.net/$web/clip/draft/20210104b/overview-b.svg")
with st.expander("What can CLIP do?"):
st.markdown("#### Prompt ranking")
st.markdown("Given different prompts and an image CLIP will rank the different prompts based on how well they describe the image")
st.markdown("#### Image ranking")
st.markdown("Given different images and a prompt CLIP will rank the different images based on how well they fit the description")
st.markdown("#### Image classification")
st.markdown("Similar to prompt ranking, given a set of classes CLIP can classify an image between them. "
"Think of [Hotdog/ Not hotdog](https://www.youtube.com/watch?v=pqTntG1RXSY&ab_channel=tvpromos) without any training.")
st.markdown(" ")
st.markdown(" ")
@staticmethod
def image_uploader(accept_multiple_files: bool):
uploaded_images = st.file_uploader("Upload image", type=[".png", ".jpg", ".jpeg"],
accept_multiple_files=accept_multiple_files)
if (not accept_multiple_files and uploaded_images is not None) or (accept_multiple_files and len(uploaded_images) >= 1):
images = []
if not accept_multiple_files:
uploaded_images = [uploaded_images]
for uploaded_image in uploaded_images:
pil_image = Image.open(uploaded_image)
pil_image = preprocess_image(pil_image)
images.append(pil_image)
st.session_state.images = images
@staticmethod
def image_picker(default_text_input: str):
col1, col2, col3 = st.columns(3)
with col1:
default_image_1 = load_image_from_url("https://cdn.pixabay.com/photo/2014/10/13/21/34/clipper-487503_960_720.jpg")
st.image(default_image_1, use_column_width=True)
if st.button("Select image 1"):
st.session_state.images = [default_image_1]
st.session_state.default_text_input = default_text_input
with col2:
default_image_2 = load_image_from_url("https://cdn.pixabay.com/photo/2019/11/11/14/30/zebra-4618513_960_720.jpg")
st.image(default_image_2, use_column_width=True)
if st.button("Select image 2"):
st.session_state.images = [default_image_2]
st.session_state.default_text_input = default_text_input
with col3:
default_image_3 = load_image_from_url("https://cdn.pixabay.com/photo/2016/11/15/16/24/banana-1826760_960_720.jpg")
st.image(default_image_3, use_column_width=True)
if st.button("Select image 3"):
st.session_state.images = [default_image_3]
st.session_state.default_text_input = default_text_input
@staticmethod
def dataset_picker():
columns = st.columns(5)
st.session_state.dataset = load_default_dataset()
image_idx = 0
for col in columns:
col.image(st.session_state.dataset[image_idx])
image_idx += 1
col.image(st.session_state.dataset[image_idx])
image_idx += 1
if st.button("Select random dataset"):
st.session_state.images = st.session_state.dataset
st.session_state.default_text_input = "A sign that says 'SLOW DOWN'"
@staticmethod
def prompts_input(input_label: str, prompt_prefix: str = ''):
raw_text_input = st.text_input(input_label,
value=st.session_state.default_text_input if st.session_state.default_text_input is not None else "")
st.session_state.is_default_text_input = raw_text_input == st.session_state.default_text_input
if raw_text_input:
st.session_state.prompts = [prompt_prefix + class_name for class_name in raw_text_input.split(";") if len(class_name) > 1]
@staticmethod
def single_image_input_preview():
st.markdown("### Preview")
col1, col2 = st.columns([1, 2])
with col1:
st.markdown("Image to classify")
if st.session_state.images is not None:
st.image(st.session_state.images[0], use_column_width=True)
else:
st.warning("Select an image")
with col2:
st.markdown("Labels to choose from")
if st.session_state.prompts is not None:
for prompt in st.session_state.prompts:
st.markdown(f"* {prompt}")
if len(st.session_state.prompts) < 2:
st.warning("At least two prompts/classes are needed")
else:
st.warning("Enter the prompts/classes to classify from")
@staticmethod
def multiple_images_input_preview():
st.markdown("### Preview")
st.markdown("Images to classify")
col1, col2, col3 = st.columns(3)
if st.session_state.images is not None:
for idx, image in enumerate(st.session_state.images):
if idx < len(st.session_state.images) / 2:
col1.image(st.session_state.images[idx], use_column_width=True)
else:
col2.image(st.session_state.images[idx], use_column_width=True)
if len(st.session_state.images) < 2:
col2.warning("At least 2 images required")
else:
col1.warning("Select an image")
with col3:
st.markdown("Query prompt")
if st.session_state.prompts is not None:
for prompt in st.session_state.prompts:
st.write(prompt)
else:
st.warning("Enter the prompt to classify")
@staticmethod
def classification_output(model: ClipModel):
if st.button("Predict") and is_valid_prediction_state():
with st.spinner("Predicting..."):
st.markdown("### Results")
if len(st.session_state.images) == 1:
scores = model.compute_prompts_probabilities(st.session_state.images[0], st.session_state.prompts)
scored_prompts = [(prompt, score) for prompt, score in zip(st.session_state.prompts, scores)]
sorted_scored_prompts = sorted(scored_prompts, key=lambda x: x[1], reverse=True)
for prompt, probability in sorted_scored_prompts:
percentage_prob = int(probability * 100)
st.markdown(
f"###  {prompt}")
elif len(st.session_state.prompts) == 1:
st.markdown(f"### {st.session_state.prompts[0]}")
scores = model.compute_images_probabilities(st.session_state.images, st.session_state.prompts[0])
scored_images = [(image, score) for image, score in zip(st.session_state.images, scores)]
sorted_scored_images = sorted(scored_images, key=lambda x: x[1], reverse=True)
for image, probability in sorted_scored_images[:5]:
col1, col2 = st.columns([1, 3])
col1.image(image, use_column_width=True)
percentage_prob = int(probability * 100)
col2.markdown(f"### ")
else:
raise ValueError("Invalid state")
# is_default_image = isinstance(state.images[0], str)
# is_default_prediction = is_default_image and state.is_default_text_input
# if is_default_prediction:
# st.markdown("<br>:information_source: Try writing your own prompts and using your own pictures!",
# unsafe_allow_html=True)
# elif is_default_image:
# st.markdown("<br>:information_source: You can also use your own pictures!",
# unsafe_allow_html=True)
# elif state.is_default_text_input:
# st.markdown("<br>:information_source: Try writing your own prompts!"
# " It can be whatever you can think of",
# unsafe_allow_html=True)
if __name__ == "__main__":
Sections.header()
col1, col2 = st.columns([1, 2])
col1.markdown(" "); col1.markdown(" ")
col1.markdown("#### Task selection")
task_name: str = col2.selectbox("", options=["Prompt ranking", "Image ranking", "Image classification"])
st.markdown("<br>", unsafe_allow_html=True)
init_state()
model = load_model(st.session_state.model_architecture)
if task_name == "Image classification":
Sections.image_uploader(accept_multiple_files=False)
if st.session_state.images is None:
st.markdown("or choose one from")
Sections.image_picker(default_text_input="banana; boat; bird")
input_label = "Enter the classes to chose from separated by a semi-colon. (f.x. `banana; boat; honesty; apple`)"
Sections.prompts_input(input_label, prompt_prefix='A picture of a ')
limit_number_images()
Sections.single_image_input_preview()
Sections.classification_output(model)
elif task_name == "Prompt ranking":
Sections.image_uploader(accept_multiple_files=False)
if st.session_state.images is None:
st.markdown("or choose one from")
Sections.image_picker(default_text_input="A calm afternoon in the Mediterranean; "
"A beautiful creature;"
" Something that grows in tropical regions")
input_label = "Enter the prompts to choose from separated by a semi-colon. " \
"(f.x. `An image that inspires; A feeling of loneliness; joyful and young; apple`)"
Sections.prompts_input(input_label)
limit_number_images()
Sections.single_image_input_preview()
Sections.classification_output(model)
elif task_name == "Image ranking":
Sections.image_uploader(accept_multiple_files=True)
if st.session_state.images is None or len(st.session_state.images) < 2:
st.markdown("or use this random dataset")
Sections.dataset_picker()
Sections.prompts_input("Enter the prompt to query the images by")
limit_number_prompts()
Sections.multiple_images_input_preview()
Sections.classification_output(model)
with st.expander("Advanced settings"):
st.session_state.model_architecture = st.selectbox("Model architecture", options=['RN50', 'RN101', 'RN50x4', 'RN50x16', 'RN50x64', 'ViT-B/32',
'ViT-B/16', 'ViT-L/14', 'ViT-L/14@336px'], index=0)
st.markdown("<br><br><br><br>Made by [@JavierFnts](https://twitter.com/JavierFnts) | [How was CLIP Playground built?](https://twitter.com/JavierFnts/status/1363522529072214019)"
"", unsafe_allow_html=True)