Merge pull request #3 from JaviFuentes94/feature--local-model

streamlit_app.py → app.py

@@ -1,59 +1,62 @@
 import random
+import requests
 
 import streamlit as st
-
-from session_state import SessionState, get_state
-from images_mocker import ImagesMocker
-
-images_mocker = ImagesMocker()
-import booste
+from clip_model import ClipModel
 
 from PIL import Image
 
-# Unfortunately Streamlit sharing does not allow to hide enviroment variables yet.
-# Do not copy this API key, go to https://www.booste.io/ and get your own, it is free!
-BOOSTE_API_KEY = "3818ba84-3526-4029-9dc8-ef3038697ea2"
-
 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/2019/10/19/12/21/hot-air-balloons-4561264_960_720.jpg",
-                # "https://cdn.pixabay.com/photo/2019/12/17/18/20/peacock-4702197_960_720.jpg",
-                # "https://cdn.pixabay.com/photo/2016/11/15/16/24/banana-1826760_960_720.jpg",
-                # "https://cdn.pixabay.com/photo/2020/12/28/22/48/buddha-5868759_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/2020/08/28/06/13/building-5523630_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",
-                # "https://cdn.pixabay.com/photo/2016/11/29/04/52/architecture-1867411_960_720.jpg",
                 ]
 
 @st.cache  # Cache this so that it doesn't change every time something changes in the page
-def select_random_dataset():
-    return random.sample(IMAGES_LINKS, 10)
+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() -> ClipModel:
+    return ClipModel()
 
+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
 
-def limit_number_images(state: SessionState):
+
+def limit_number_images():
     """When moving between tasks sometimes the state of images can have too many samples"""
-    if state.images is not None and len(state.images) > 1:
-        state.images = [state.images[0]]
+    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(state: SessionState):
+def limit_number_prompts():
     """When moving between tasks sometimes the state of prompts can have too many samples"""
-    if state.prompts is not None and len(state.prompts) > 1:
-        state.prompts = [state.prompts[0]]
+    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(state: SessionState) -> bool:
-    if state.images is None or len(state.images) < 1:
+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 state.prompts is None or len(state.prompts) < 1:
+    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
@@ -101,16 +104,16 @@ class Sections:
         st.markdown("### Try OpenAI's CLIP model in your browser")
         st.markdown(" ")
         st.markdown(" ")
-        with st.beta_expander("What is CLIP?"):
+        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.beta_columns(2)
+            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.beta_expander("What can CLIP do?"):
+        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")
@@ -122,7 +125,7 @@ class Sections:
         st.markdown(" ")
 
     @staticmethod
-    def image_uploader(state: SessionState, accept_multiple_files: bool):
+    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):
@@ -133,199 +136,181 @@ class Sections:
                 pil_image = Image.open(uploaded_image)
                 pil_image = preprocess_image(pil_image)
                 images.append(pil_image)
-            state.images = images
+            st.session_state.images = images
 
 
     @staticmethod
-    def image_picker(state: SessionState, default_text_input: str):
-        col1, col2, col3 = st.beta_columns(3)
+    def image_picker(default_text_input: str):
+        col1, col2, col3 = st.columns(3)
         with col1:
-            default_image_1 = "https://cdn.pixabay.com/photo/2014/10/13/21/34/clipper-487503_960_720.jpg"
+            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"):
-                state.images = [default_image_1]
-                state.default_text_input = default_text_input
+                st.session_state.images = [default_image_1]
+                st.session_state.default_text_input = default_text_input
         with col2:
-            default_image_2 = "https://cdn.pixabay.com/photo/2019/11/11/14/30/zebra-4618513_960_720.jpg"
+            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"):
-                state.images = [default_image_2]
-                state.default_text_input = default_text_input
+                st.session_state.images = [default_image_2]
+                st.session_state.default_text_input = default_text_input
         with col3:
-            default_image_3 = "https://cdn.pixabay.com/photo/2016/11/15/16/24/banana-1826760_960_720.jpg"
+            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"):
-                state.images = [default_image_3]
-                state.default_text_input = default_text_input
+                st.session_state.images = [default_image_3]
+                st.session_state.default_text_input = default_text_input
 
     @staticmethod
-    def dataset_picker(state: SessionState):
-        columns = st.beta_columns(5)
-        state.dataset = select_random_dataset()
+    def dataset_picker():
+        columns = st.columns(5)
+        st.session_state.dataset = load_default_dataset()
         image_idx = 0
         for col in columns:
-            col.image(state.dataset[image_idx])
+            col.image(st.session_state.dataset[image_idx])
             image_idx += 1
-            col.image(state.dataset[image_idx])
+            col.image(st.session_state.dataset[image_idx])
             image_idx += 1
         if st.button("Select random dataset"):
-            state.images = state.dataset
-            state.default_text_input = "A sign that says 'SLOW DOWN'"
+            st.session_state.images = st.session_state.dataset
+            st.session_state.default_text_input = "A sign that says 'SLOW DOWN'"
 
     @staticmethod
-    def prompts_input(state: SessionState, input_label: str, prompt_prefix: str = ''):
+    def prompts_input(input_label: str, prompt_prefix: str = ''):
         raw_text_input = st.text_input(input_label,
-                                    value=state.default_text_input if state.default_text_input is not None else "")
-        state.is_default_text_input = raw_text_input == state.default_text_input
+                                    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:
-            state.prompts = [prompt_prefix + class_name for class_name in raw_text_input.split(";") if len(class_name) > 1]
+            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(state: SessionState):
+    def single_image_input_preview():
         st.markdown("### Preview")
-        col1, col2 = st.beta_columns([1, 2])
+        col1, col2 = st.columns([1, 2])
         with col1:
             st.markdown("Image to classify")
-            if state.images is not None:
-                st.image(state.images[0], use_column_width=True)
+            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 state.prompts is not None:
-                for prompt in state.prompts:
+            if st.session_state.prompts is not None:
+                for prompt in st.session_state.prompts:
                     st.markdown(f"* {prompt}")
-                if len(state.prompts) < 2:
+                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(state: SessionState):
+    def multiple_images_input_preview():
         st.markdown("### Preview")
         st.markdown("Images to classify")
-        col1, col2, col3 = st.beta_columns(3)
-        if state.images is not None:
-            for idx, image in enumerate(state.images):
-                if idx < len(state.images) / 2:
-                    col1.image(state.images[idx], use_column_width=True)
+        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(state.images[idx], use_column_width=True)
-            if len(state.images) < 2:
+                    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 state.prompts is not None:
-                for prompt in state.prompts:
+            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(state: SessionState):
-        # Possible way of customize this https://discuss.streamlit.io/t/st-button-in-a-custom-layout/2187/2
-        if st.button("Predict") and is_valid_prediction_state(state):  # PREDICT 🚀
+    def classification_output(model: ClipModel):
+        if st.button("Predict") and is_valid_prediction_state():
             with st.spinner("Predicting..."):
-                if isinstance(state.images[0], str):
-                    clip_response = booste.clip(BOOSTE_API_KEY,
-                                                prompts=state.prompts,
-                                                images=state.images)
-                else:
-                    images_mocker.calculate_image_id2image_lookup(state.images)
-                    images_mocker.start_mocking()
-                    clip_response = booste.clip(BOOSTE_API_KEY,
-                                                prompts=state.prompts,
-                                                images=images_mocker.image_ids)
-                    images_mocker.stop_mocking()
+                
                 st.markdown("### Results")
-                # st.write(clip_response)
-                if len(state.images) == 1:
-                    simplified_clip_results = [(prompt,
-                                                list(results.values())[0]["probabilityRelativeToPrompts"])
-                                               for prompt, results in clip_response.items()]
-                    simplified_clip_results = sorted(simplified_clip_results, key=lambda x: x[1], reverse=True)
-
-                    for prompt, probability in simplified_clip_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"### ![prob](https://progress-bar.dev/{percentage_prob}/?width=200) &nbsp &nbsp {prompt}")
-                else:
-                    st.markdown(f"### {state.prompts[0]}")
-                    assert len(state.prompts) == 1
-                    if isinstance(state.images[0], str):
-                        simplified_clip_results = [(image, results["probabilityRelativeToImages"]) for image, results
-                                                   in list(clip_response.values())[0].items()]
-                    else:
-                        simplified_clip_results = [(images_mocker.image_id2image(image),
-                                                    results["probabilityRelativeToImages"]) for image, results
-                                                   in list(clip_response.values())[0].items()]
-                    simplified_clip_results = sorted(simplified_clip_results, key=lambda x: x[1], reverse=True)
-                    for image, probability in simplified_clip_results[:5]:
-                        col1, col2 = st.beta_columns([1, 3])
+                            f"### ![prob](https://progress-bar.dev/{percentage_prob}/?width=200) {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"### ![prob](https://progress-bar.dev/{percentage_prob}/?width=200)")
-                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)
-
-
-Sections.header()
-col1, col2 = st.beta_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)
-
-images_mocker.stop_mocking()  # Sometimes it gets stuck mocking
-
-session_state = get_state()
-if task_name == "Image classification":
-    Sections.image_uploader(session_state, accept_multiple_files=False)
-    if session_state.images is None:
-        st.markdown("or choose one from")
-        Sections.image_picker(session_state, 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(session_state, input_label, prompt_prefix='A picture of a ')
-    limit_number_images(session_state)
-    Sections.single_image_input_preview(session_state)
-    Sections.classification_output(session_state)
-elif task_name == "Prompt ranking":
-    Sections.image_uploader(session_state, accept_multiple_files=False)
-    if session_state.images is None:
-        st.markdown("or choose one from")
-        Sections.image_picker(session_state, 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(session_state, input_label)
-    limit_number_images(session_state)
-    Sections.single_image_input_preview(session_state)
-    Sections.classification_output(session_state)
-elif task_name == "Image ranking":
-    Sections.image_uploader(session_state, accept_multiple_files=True)
-    if session_state.images is None or len(session_state.images) < 2:
-        st.markdown("or use this random dataset")
-        Sections.dataset_picker(session_state)
-    Sections.prompts_input(session_state, "Enter the prompt to query the images by")
-    limit_number_prompts(session_state)
-    Sections.multiple_images_input_preview(session_state)
-    Sections.classification_output(session_state)
-
-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)
-session_state.sync()
+                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()
+    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)
+
+    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)

clip_model.py

@@ -0,0 +1,71 @@
+import clip
+from PIL.Image import Image
+import torch
+
+
+
+class ClipModel:
+    def __init__(self, model_name: str = 'RN50') -> None:
+        """
+        Available models
+        ['RN50', 'RN101', 'RN50x4', 'RN50x16', 'RN50x64', 'ViT-B/32',
+         'ViT-B/16', 'ViT-L/14', 'ViT-L/14@336px']
+        """
+        self._model, self._img_preprocess = clip.load(model_name)
+    
+    def predict(self, images: list[Image], prompts: list[str]) -> dict:
+        if len(images) == 1:
+            return self.compute_prompts_probabilities(images[0], prompts)
+        elif len(prompts) == 1:
+            return self.compute_images_probabilities(images, prompts[0])
+        else:
+            raise ValueError('Either images or prompts must be a single element')
+    
+    def compute_prompts_probabilities(self, image: Image, prompts: list[str]) -> list[float]:
+        preprocessed_image = self._img_preprocess(image).unsqueeze(0)
+        tokenized_prompts = clip.tokenize(prompts)
+        with torch.inference_mode():
+            image_features = self._model.encode_image(preprocessed_image)
+            text_features = self._model.encode_text(tokenized_prompts)
+
+            # normalized features
+            image_features = image_features / image_features.norm(dim=1, keepdim=True)
+            text_features = text_features / text_features.norm(dim=1, keepdim=True)
+
+            # cosine similarity as logits
+            logit_scale = self._model.logit_scale.exp()
+            logits_per_image = logit_scale * image_features @ text_features.t()
+
+            probs = list(logits_per_image.softmax(dim=-1).cpu().numpy()[0])
+
+        return probs
+    
+    def compute_images_probabilities(self, images: list[Image], prompt: str) -> list[float]:
+        preprocessed_images = [self._img_preprocess(image).unsqueeze(0) for image in images]
+        tokenized_prompts = clip.tokenize(prompt)
+        with torch.inference_mode():
+            image_features = torch.cat([self._model.encode_image(preprocessed_image) for preprocessed_image in preprocessed_images])
+            text_features = self._model.encode_text(tokenized_prompts)
+
+            # normalized features
+            image_features = image_features / image_features.norm(dim=1, keepdim=True)
+            text_features = text_features / text_features.norm(dim=1, keepdim=True)
+
+            # cosine similarity as logits
+            logit_scale = self._model.logit_scale.exp()
+            logits_per_image = logit_scale * text_features @ image_features.t()
+
+            probs = list(logits_per_image.softmax(dim=-1).cpu().numpy()[0])
+
+        return probs
+
+if __name__ == "__main__":
+    from app import load_default_dataset
+
+    model = ClipModel()
+    images = load_default_dataset()
+    prompts = ['Hello', 'How are you', 'Goodbye']
+    prompts_scores = model.compute_prompts_probabilities(images[0], prompts)
+    images_scores = model.compute_images_probabilities(images, prompts[0])
+    print(f"Prompts scores: {prompts_scores}")
+    print(f"Images scores: {images_scores}")

requirements.txt

@@ -1,4 +1,5 @@
-streamlit~=0.76.0
-booste==0.2.8
+streamlit~=1.11.1
+git+https://github.com/openai/CLIP@b46f5ac
 Pillow==8.1.0
-mock==4.0.3
+mock==4.0.3
+protobuf==3.20.0  # It raises errors otherwise