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import json |
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import gradio as gr |
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import pandas as pd |
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from app import fs |
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from src.constants import SUBTASKS, DETAILS_DATASET_ID, DETAILS_FILENAME |
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def update_subtasks_component(task): |
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return gr.Radio( |
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SUBTASKS.get(task), |
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info="Evaluation subtasks to be displayed", |
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value=None, |
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) |
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def update_load_details_component(model_id_1, model_id_2, subtask): |
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if (model_id_1 or model_id_2) and subtask: |
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return gr.Button("Load Details", interactive=True) |
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else: |
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return gr.Button("Load Details", interactive=False) |
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def load_details_dataframe(model_id, subtask): |
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if not model_id or not subtask: |
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return |
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model_name_sanitized = model_id.replace("/", "__") |
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paths = fs.glob( |
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f"{DETAILS_DATASET_ID}/**/{DETAILS_FILENAME}".format( |
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model_name_sanitized=model_name_sanitized, subtask=subtask |
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) |
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) |
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if not paths: |
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return |
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path = max(paths) |
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with fs.open(path, "r") as f: |
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data = [json.loads(line) for line in f] |
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df = pd.json_normalize(data) |
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df["model_name"] = model_id |
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return df |
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def load_details_dataframes(subtask, *model_ids): |
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return [load_details_dataframe(model_id, subtask) for model_id in model_ids] |
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def display_details(sample_idx, *dfs): |
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rows = [df.iloc[sample_idx] for df in dfs if "model_name" in df.columns and sample_idx < len(df)] |
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if not rows: |
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return |
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df = pd.concat([row.rename(row.pop("model_name")) for row in rows], axis="columns") |
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return ( |
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df.style |
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.format(na_rep="") |
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.to_html() |
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) |
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def update_sample_idx_component(*dfs): |
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maximum = max([len(df) - 1 for df in dfs]) |
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return gr.Number( |
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label="Sample Index", |
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info="Index of the sample to be displayed", |
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value=0, |
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minimum=0, |
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maximum=maximum, |
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visible=True, |
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) |
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def clear_details(): |
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return ( |
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None, None, None, None, None, None, |
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gr.Number(label="Sample Index", info="Index of the sample to be displayed", value=0, minimum=0,visible=False), |
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) |
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