packageify

.gitignore

@@ -2,6 +2,7 @@
 .DS_Store
 .vscode/
 .vim/
+flagged/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

Dockerfile

@@ -1,5 +1,8 @@
 FROM python:3.8.9
 
+ENV DEBIAN_FRONTEND=noninteractive \
+    TZ=Europe/Paris
+
 RUN pip install pip==22.3.1
 
 WORKDIR /app
@@ -9,6 +12,15 @@ RUN chown -R 1000:1000 /app
 USER 1000
 ENV HOME=/app
 
+ENV PYTHONPATH=$HOME/app \
+    PYTHONUNBUFFERED=1 \
+    GRADIO_ALLOW_FLAGGING=never \
+    GRADIO_NUM_PORTS=1 \
+    GRADIO_SERVER_NAME=0.0.0.0 \
+    GRADIO_THEME=huggingface \
+    SYSTEM=spaces
+
+
 RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
     && sh Miniconda3-latest-Linux-x86_64.sh -b -p /app/miniconda \
     && rm -f Miniconda3-latest-Linux-x86_64.sh
@@ -16,6 +28,7 @@ ENV PATH /app/miniconda/bin:$PATH
 
 RUN conda create -p /app/env -y python=3.8
 
+
 SHELL ["conda", "run","--no-capture-output", "-p","/app/env", "/bin/bash", "-c"]
 
 COPY --chown=1000:1000 requirements.txt /app/requirements.txt
@@ -24,8 +37,4 @@ RUN pip install -r requirements.txt
 COPY --chown=1000:1000 *.py /app/
 COPY --chown=1000:1000 pages/ /app/pages/
 
-CMD streamlit run Overview.py \
-    --server.headless true \
-    --server.enableCORS false \
-    --server.enableXsrfProtection false \
-    --server.fileWatcherType none
+CMD python app.py

LICENSE

@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
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+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
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+   6. Trademarks. This License does not grant permission to use the trade
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+
+   7. Disclaimer of Warranty. Unless required by applicable law or
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+      Contributor provides its Contributions) on an "AS IS" BASIS,
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+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
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+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
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+   Copyright [2022] [Hugging Face Inc.]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

Overview.py

@@ -1,20 +0,0 @@
-import streamlit as st
-
-import config
-
-st.set_page_config(
-    page_title="Overview",
-    page_icon="🤗",
-)
-
-st.write(f"# Welcome to {config.competition_info.competition_name}! 👋")
-
-st.markdown(
-    f"""
-    {config.competition_info.competition_description}
-"""
-)
-
-st.markdown("## Dataset")
-# st.sidebar.header("Dataset")
-st.write(f"""{config.competition_info.dataset_description}""")

__init__.py → competitions/__init__.py

@@ -1,3 +1,4 @@
 import config
 
+
 __version__ = "0.0.1"

app.py → competitions/app.py

@@ -2,14 +2,13 @@ import uuid
 from datetime import datetime
 from functools import partial
 
+import config
 import gradio as gr
-import numpy as np
 import pandas as pd
+import utils
 from huggingface_hub import HfApi
 from huggingface_hub.utils._errors import EntryNotFoundError
 
-import config
-import utils
 
 SUBMISSION_TEXT = f"""You can make upto {config.competition_info.submission_limit} submissions per day.
 The test data has been divided into public and private splits.
@@ -32,7 +31,7 @@ def get_subs(user_info, private=False):
         user_submissions = utils.fetch_submissions(user_id)
     except EntryNotFoundError:
         return_value = "No submissions found"
-        return [gr.Textbox.update(visible=True, value=return_value), gr.DataFrame.update(visible=False)]
+        return [gr.Markdown.update(visible=True, value=return_value), gr.DataFrame.update(visible=False)]
     submissions_df = pd.DataFrame(user_submissions)
     if not private:
         submissions_df = submissions_df.drop(columns=["private_score"])
@@ -43,20 +42,19 @@ def get_subs(user_info, private=False):
         submissions_df = submissions_df[
             ["date", "submission_id", "public_score", "private_score", "submission_comment", "selected", "status"]
         ]
-    return [gr.Textbox.update(visible=False), gr.DataFrame.update(visible=True, value=submissions_df)]
+    return [gr.Markdown.update(visible=False), gr.DataFrame.update(visible=True, value=submissions_df)]
 
 
 def my_submissions(user_token):
     if user_token != "":
         user_info = utils.user_authentication(token=user_token)
-        print(user_info)
         if "error" in user_info:
             return_value = "Invalid token"
-            return [gr.Textbox.update(visible=True, value=return_value), gr.DataFrame.update(visible=False)]
+            return [gr.Markdown.update(visible=True, value=return_value), gr.DataFrame.update(visible=False)]
 
         if user_info["emailVerified"] is False:
             return_value = "Please verify your email on Hugging Face Hub"
-            return [gr.Textbox.update(visible=True, value=return_value), gr.DataFrame.update(visible=False)]
+            return [gr.Markdown.update(visible=True, value=return_value), gr.DataFrame.update(visible=False)]
 
         current_date_time = datetime.now()
         private = False
@@ -64,41 +62,34 @@ def my_submissions(user_token):
             private = True
         subs = get_subs(user_info, private=private)
         return subs
-    return [gr.Textbox.update(visible=True, value="Invalid token"), gr.DataFrame.update(visible=False)]
+    return [gr.Markdown.update(visible=True, value="Invalid token"), gr.DataFrame.update(visible=False)]
 
 
-def new_submission(user_token):
-    gr.Markdown(SUBMISSION_TEXT)
-    uploaded_file = st.file_uploader("Choose a file")
-    submit_button = st.button("Submit")
-    if uploaded_file is not None and user_token != "" and submit_button:
+def new_submission(user_token, uploaded_file):
+    if uploaded_file is not None and user_token != "":
         # verify token
         user_info = utils.user_authentication(token=user_token)
         if "error" in user_info:
-            gr.Markdown("Invalid token")
-            return
+            return "Invalid token"
 
         if user_info["emailVerified"] is False:
-            gr.Markdown("Please verify your email on Hugging Face Hub")
-            return
+            return "Please verify your email on Hugging Face Hub"
 
         # check if user can submit to the competition
         if utils.check_user_submission_limit(user_info) is False:
-            gr.Markdown("You have reached your submission limit for today")
-            return
-
-        bytes_data = uploaded_file.getvalue()
+            return "You have reached your submission limit for today"
+        with open(uploaded_file.name, "rb") as f:
+            bytes_data = f.read()
         # verify file is valid
         if not utils.verify_submission(bytes_data):
-            gr.Markdown("Invalid submission")
-            gr.Markdown(SUBMISSION_ERROR)
+            return "Invalid submission"
+            # gr.Markdown(SUBMISSION_ERROR)
             # write a horizontal html line
-            gr.Markdown("<hr/>", unsafe_allow_html=True)
+            # gr.Markdown("<hr/>", unsafe_allow_html=True)
         else:
-            # TODO: add spinner here
             user_id = user_info["id"]
             submission_id = str(uuid.uuid4())
-            file_extension = uploaded_file.name.split(".")[-1]
+            file_extension = uploaded_file.orig_name.split(".")[-1]
             # upload file to hf hub
             api = HfApi()
             api.upload_file(
@@ -121,10 +112,9 @@ def new_submission(user_token):
                 submission_dataset=user_id,
                 model="generic_competition",
             )
-        gr.Markdown("Submission scheduled for evaluation")
-        gr.Markdown(
-            f"You have {config.competition_info.submission_limit - submissions_made} submissions left for today."
-        )
+        return_text = f"Submission scheduled for evaluation. You have {config.competition_info.submission_limit - submissions_made} submissions left for today."
+        return return_text
+    return "Error"
 
 
 with gr.Blocks() as demo:
@@ -137,35 +127,32 @@ with gr.Blocks() as demo:
         gr.Markdown(f"{config.competition_info.dataset_description}")
 
     with gr.Tab("Public Leaderboard"):
-        lb = utils.fetch_leaderboard(private=False)
-        gr.Markdown(lb.to_markdown())
+        output_markdown = gr.Markdown("")
+        fetch_lb = gr.Button("Fetch Leaderboard")
+        fetch_lb_partial = partial(utils.fetch_leaderboard, private=False)
+        fetch_lb.click(fn=fetch_lb_partial, outputs=[output_markdown])
+        # lb = utils.fetch_leaderboard(private=False)
+        # gr.Markdown(lb.to_markdown())
     with gr.Tab("Private Leaderboard"):
         current_date_time = datetime.now()
         if current_date_time >= config.competition_info.end_date:
             lb = utils.fetch_leaderboard(private=True)
-            gr.Markdown(lb.to_markdown())
+            gr.Markdown(lb)
         else:
             gr.Markdown("Private Leaderboard will be available after the competition ends")
     with gr.Tab("New Submission"):
-        text_input = gr.Textbox()
-        text_output = gr.Textbox()
-        text_button = gr.Button("Flip")
+        gr.Markdown(SUBMISSION_TEXT)
+        user_token = gr.Textbox(max_lines=1, value="hf_XXX", label="Please enter your Hugging Face token")
+        uploaded_file = gr.File()
+        output_text = gr.Markdown(visible=True, show_label=False)
+        new_sub_button = gr.Button("Upload Submission")
+        new_sub_button.click(fn=new_submission, inputs=[user_token, uploaded_file], outputs=[output_text])
+
     with gr.Tab("My Submissions"):
         gr.Markdown(SUBMISSION_LIMIT_TEXT)
         user_token = gr.Textbox(max_lines=1, value="hf_XXX", label="Please enter your Hugging Face token")
-        output_text = gr.Textbox(visible=True, show_label=False)
-        empty_df = pd.DataFrame(
-            columns=[
-                "date",
-                "submission_id",
-                "public_score",
-                "private_score",
-                "submission_comment",
-                "selected",
-                "status",
-            ]
-        )
-        output_df = gr.Dataframe(visible=False, value=empty_df)
+        output_text = gr.Markdown(visible=True, show_label=False)
+        output_df = gr.Dataframe(visible=False)
         my_subs_button = gr.Button("Fetch Submissions")
         my_subs_button.click(fn=my_submissions, inputs=[user_token], outputs=[output_text, output_df])
 

config.py → competitions/config.py

@@ -7,6 +7,7 @@ from dotenv import load_dotenv
 from huggingface_hub import hf_hub_download
 from huggingface_hub.utils._errors import EntryNotFoundError
 
+
 if Path(".env").is_file():
     load_dotenv(".env")
 

utils.py → competitions/utils.py

@@ -5,13 +5,12 @@ import json
 import os
 import time
 
+import config
 import pandas as pd
 import requests
 from huggingface_hub import HfApi, hf_hub_download, snapshot_download
 from huggingface_hub.utils._errors import EntryNotFoundError
 
-import config
-
 
 def get_auth_headers(token: str, prefix: str = "Bearer"):
     return {"Authorization": f"{prefix} {token}"}
@@ -303,4 +302,4 @@ def fetch_leaderboard(private=False):
         columns = ["rank", "name", "private_score", "submission_datetime"]
     else:
         columns = ["rank", "name", "public_score", "submission_datetime"]
-    return df[columns]
+    return df[columns].to_markdown()

pages/1_🔥_Submissions.py

@@ -1,142 +0,0 @@
-import uuid
-from datetime import datetime
-
-import pandas as pd
-import streamlit as st
-from huggingface_hub import HfApi
-from huggingface_hub.utils._errors import EntryNotFoundError
-
-import config
-import utils
-
-SUBMISSION_TEXT = f"""You can make upto {config.competition_info.submission_limit} submissions per day.
-The test data has been divided into public and private splits.
-Your score on the public split will be shown on the leaderboard.
-Your final score will be based on your private split performance.
-The final rankings will be based on the private split performance.
-"""
-
-SUBMISSION_ERROR = """Submission is not in a proper format.
-Please check evaluation instructions for more details."""
-
-SUBMISSION_LIMIT_TEXT = f"""You can select upto {config.competition_info.selection_limit}
- submissions for private leaderboard."""
-
-
-def get_subs(user_info, private=False):
-    # get user submissions
-    user_id = user_info["id"]
-    try:
-        user_submissions = utils.fetch_submissions(user_id)
-    except EntryNotFoundError:
-        st.error("No submissions found")
-        return
-    submissions_df = pd.DataFrame(user_submissions)
-    if not private:
-        submissions_df = submissions_df.drop(columns=["private_score"])
-        submissions_df = submissions_df[
-            ["date", "submission_id", "public_score", "submission_comment", "selected", "status"]
-        ]
-    else:
-        submissions_df = submissions_df[
-            ["date", "submission_id", "public_score", "private_score", "submission_comment", "selected", "status"]
-        ]
-    st.write(submissions_df)
-
-
-def new_submission(user_token):
-    st.markdown(SUBMISSION_TEXT)
-    uploaded_file = st.file_uploader("Choose a file")
-    submit_button = st.button("Submit")
-    if uploaded_file is not None and user_token != "" and submit_button:
-        # verify token
-        user_info = utils.user_authentication(token=user_token)
-        if "error" in user_info:
-            st.error("Invalid token")
-            return
-
-        if user_info["emailVerified"] is False:
-            st.error("Please verify your email on Hugging Face Hub")
-            return
-
-        # check if user can submit to the competition
-        if utils.check_user_submission_limit(user_info) is False:
-            st.error("You have reached your submission limit for today")
-            return
-
-        bytes_data = uploaded_file.getvalue()
-        # verify file is valid
-        if not utils.verify_submission(bytes_data):
-            st.error("Invalid submission")
-            st.write(SUBMISSION_ERROR)
-            # write a horizontal html line
-            st.markdown("<hr/>", unsafe_allow_html=True)
-        else:
-            with st.spinner("Creating submission... Please wait"):
-                user_id = user_info["id"]
-                submission_id = str(uuid.uuid4())
-                file_extension = uploaded_file.name.split(".")[-1]
-                # upload file to hf hub
-                api = HfApi()
-                api.upload_file(
-                    path_or_fileobj=bytes_data,
-                    path_in_repo=f"submissions/{user_id}-{submission_id}.{file_extension}",
-                    repo_id=config.COMPETITION_ID,
-                    repo_type="dataset",
-                    token=config.AUTOTRAIN_TOKEN,
-                )
-                # update submission limit
-                submissions_made = utils.increment_submissions(
-                    user_id=user_id,
-                    submission_id=submission_id,
-                    submission_comment="",
-                )
-                # schedule submission for evaluation
-                utils.create_project(
-                    project_id=f"{submission_id}",
-                    dataset=f"{config.COMPETITION_ID}",
-                    submission_dataset=user_id,
-                    model="generic_competition",
-                )
-            st.success("Submission scheduled for evaluation")
-            st.success(
-                f"You have {config.competition_info.submission_limit - submissions_made} submissions left for today."
-            )
-
-
-def my_submissions(user_token):
-    st.markdown(SUBMISSION_LIMIT_TEXT)
-    if user_token != "":
-        user_info = utils.user_authentication(token=user_token)
-        if "error" in user_info:
-            st.error("Invalid token")
-            return
-
-        if user_info["emailVerified"] is False:
-            st.error("Please verify your email on Hugging Face Hub")
-            return
-
-        current_date_time = datetime.now()
-        private = False
-        if current_date_time >= config.competition_info.end_date:
-            private = True
-        get_subs(user_info, private=private)
-
-
-def app():
-    st.set_page_config(page_title="Submissions", page_icon="🤗")
-    st.markdown("## Submissions")
-    # enter token link to: https://huggingface.co/settings/tokens
-    user_token = st.text_input("Enter your Hugging Face token", value="", type="password")
-    user_token = user_token.strip()
-    new_sub, my_sub = st.tabs(["New Submission", "My Submissions"])
-
-    with new_sub:
-        new_submission(user_token)
-
-    with my_sub:
-        my_submissions(user_token)
-
-
-if __name__ == "__main__":
-    app()

pages/2_📝_Leaderboard.py

@@ -1,28 +0,0 @@
-from datetime import datetime
-
-import streamlit as st
-
-import config
-import utils
-
-
-def app():
-    st.set_page_config(page_title="Leaderboard", page_icon="🤗")
-    st.markdown("## Leaderboard")
-    public_lb, private_lb = st.tabs(["Public", "Private"])
-    current_date_time = datetime.now()
-
-    with public_lb:
-        lb = utils.fetch_leaderboard(private=False)
-        st.table(lb)
-
-    with private_lb:
-        if current_date_time >= config.competition_info.end_date:
-            lb = utils.fetch_leaderboard(private=True)
-            st.table(lb)
-        else:
-            st.error("Private Leaderboard will be available after the competition ends")
-
-
-if __name__ == "__main__":
-    app()

requirements.txt

@@ -1,4 +1,5 @@
 gradio==3.9.1
 pandas==1.5.0
 python-dotenv==0.20.0
-huggingface_hub==0.10.1
+huggingface_hub==0.10.1
+tabulate==0.9.0

setup.cfg

@@ -0,0 +1,16 @@
+[metadata]
+license_file = LICENSE
+version = attr: competitions.__version__
+
+[isort]
+ensure_newline_before_comments = True
+force_grid_wrap = 0
+include_trailing_comma = True
+line_length = 119
+lines_after_imports = 2
+multi_line_output = 3
+use_parentheses = True
+
+[flake8]
+ignore = E203, E501, W503
+max-line-length = 119

setup.py

@@ -0,0 +1,60 @@
+# coding=utf-8
+# Copyright 2022 Hugging Face Inc
+#
+# Lint as: python3
+# pylint: enable=line-too-long
+"""Hugging Face Competitions
+"""
+from setuptools import find_packages, setup
+
+
+with open("README.md") as f:
+    long_description = f.read()
+
+QUALITY_REQUIRE = [
+    "black~=22.0",
+    "isort==5.8.0",
+    "flake8==3.9.2",
+    "mypy==0.901",
+]
+
+TEST_REQUIRE = ["pytest", "pytest-cov"]
+
+EXTRAS_REQUIRE = {
+    "dev": QUALITY_REQUIRE,
+    "quality": QUALITY_REQUIRE,
+    "test": TEST_REQUIRE,
+    "docs": [
+        "recommonmark",
+        "sphinx==3.1.2",
+        "sphinx-markdown-tables",
+        "sphinx-rtd-theme==0.4.3",
+        "sphinx-copybutton",
+    ],
+}
+
+with open("requirements.txt") as f:
+    INSTALL_REQUIRES = f.read().splitlines()
+
+setup(
+    name="competitions",
+    description="Hugging Face Competitions",
+    long_description=long_description,
+    author="HuggingFace Inc.",
+    url="https://github.com/huggingface/competitions",
+    download_url="https://github.com/huggingface/competitions/tags",
+    packages=find_packages("."),
+    install_requires=INSTALL_REQUIRES,
+    extras_require=EXTRAS_REQUIRE,
+    python_requires=">=3.8",
+    classifiers=[
+        "Intended Audience :: Developers",
+        "Intended Audience :: Education",
+        "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: Apache Software License",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python :: 3.8",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    ],
+    keywords="huggingface competitions machine learning ai nlp",
+)