skeleton

app.py

@@ -1,7 +1,49 @@
+import datetime
+import random
+import uuid
+from os import PathLike
+
 import gradio as gr
+import pandas as pd
+
+from config import APP_CONFIG
+from data_repository import REPOSITORY_INSTANCE, ModelScoringResult
+from designs_submission_validations import validate_github_link, validate_user_designs
+
+def compute_scores(user_gen_designs: pd.DataFrame) -> ModelScoringResult:
+    return ModelScoringResult(
+        score=random.randint(50, 5000),
+        scoring_time=datetime.datetime.now(),
+        submission_time=datetime.datetime.now(),
+        uuid=str(uuid.uuid4())
+    )
+
+
+def process_generated_designs(github_link: str, file: PathLike[str]):
+    uuid4 = uuid.uuid4()
+    validate_github_link(github_link)
+    with open(file, 'r') as user_file:
+        user_gen_designs = pd.read_csv(user_file)
+    validate_user_designs(user_gen_designs)
+    REPOSITORY_INSTANCE.add_row(compute_scores(user_gen_designs))
+    return "File uploaded successfully, uuid {uuid4}"
+
+
+with gr.Blocks() as gradio_app:
+    with gr.Tab("Bike Bench Leaderboard"):
+        gr.Markdown("Hello beautiful people!")
+        gr.Dataframe(REPOSITORY_INSTANCE.get_data_to_display(), label="Scores of Previous Files")
 
-def greet(name):
-    return "Hello " + name + "!!"
+    with gr.Tab("Upload File"):
+        gr.Interface(
+            fn=process_generated_designs,
+            inputs=[
+                gr.Textbox(label="Github Link"),
+                gr.File(label="Upload a file"),
+            ],
+            outputs="text",
+            title="Bike Bench Leaderboard",
+            description="Upload a file to see the result."
+        )
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+gradio_app.launch(debug=(not APP_CONFIG.production))

config.py

@@ -0,0 +1,22 @@
+import os
+
+import attrs
+
+
+@attrs.define(frozen=True)
+class AppConfig:
+    production: bool
+
+def get_env_or_warn(key: str):
+    result = os.getenv(key)
+
+    return result
+
+
+def get_app_config() -> AppConfig:
+    return AppConfig(
+        production=os.getenv("DEPLOYMENT_ENVIRONMENT").upper() == "PRODUCTION"
+    )
+
+
+APP_CONFIG = get_app_config()

data_repository.py

@@ -0,0 +1,66 @@
+import abc
+import datetime
+import os.path
+from typing import List
+
+import attrs
+import pandas as pd
+
+from config import AppConfig
+
+
+@attrs.define
+class ModelScoringResult:
+    uuid: str
+    score: float
+    submission_time: datetime.datetime
+    scoring_time: datetime.datetime
+
+
+ORDERED_COLUMNS = [
+    "model_id",
+    "model_score",
+    "submission_time",
+]
+
+
+
+class ModelScoresRepository(metaclass=abc.ABCMeta):
+
+    @abc.abstractmethod
+    def get_data_to_display(self) -> pd.DataFrame:
+        pass
+
+    @abc.abstractmethod
+    def add_row(self, row: ModelScoringResult) -> None:
+        pass
+
+    @abc.abstractmethod
+    def save_current_state(self, rows: List[ModelScoringResult]) -> None:
+        pass
+
+
+class LocalModelScoresRepository(ModelScoresRepository):
+    def __init__(self, dummy_file_path=os.path.join(os.path.dirname(__file__), "dummy_data.txt")):
+        self.dummy_file_path = dummy_file_path
+        if not os.path.exists(self.dummy_file_path):
+            with open(self.dummy_file_path, "w") as file:
+                file.write("uuid,score,submission_time,scoring_time")
+
+    def get_data_to_display(self):
+        return pd.DataFrame(pd.read_csv(self.dummy_file_path), columns=ORDERED_COLUMNS)
+
+    def add_row(self, row: ModelScoringResult):
+        previous_state = pd.read_csv(self.dummy_file_path)
+        result = pd.concat([previous_state, pd.DataFrame(row.as_dict())])
+        result.to_csv(self.dummy_file_path)
+
+    def save_current_state(self, rows: List[ModelScoringResult]):
+        pd.DataFrame([r.as_dict() for r in rows]).to_csv(self.dummy_file_path)
+
+
+class HuggingFaceDatasetModelScoresRepository(ModelScoresRepository):
+    pass
+
+
+REPOSITORY_INSTANCE: ModelScoresRepository = HuggingFaceDatasetModelScoresRepository() if AppConfig.production else LocalModelScoresRepository()

designs_submission_validations.py

@@ -0,0 +1,9 @@
+import pandas as pd
+
+
+def validate_github_link(github_link: str):
+    pass
+
+
+def validate_user_designs(user_generated_designs: pd.DataFrame):
+    pass