merve HF staff commited on
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fffd448
1 Parent(s): a7fabf5

Create app.py

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  1. app.py +106 -0
app.py ADDED
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+ import requests
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+ import json
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+ import pandas as pd
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+ from tqdm.auto import tqdm
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+
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+ import streamlit as st
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+ from huggingface_hub import HfApi, hf_hub_download
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+ from huggingface_hub.repocard import metadata_load
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+ import streamlit.components.v1 as components
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+
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+
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+ def make_clickable_model(model_name):
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+ link = "https://huggingface.co/" + model_name
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+ return f'<a target="_blank" href="{link}">{model_name}</a>'
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+
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+ # Make user clickable link
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+ def make_clickable_user(user_id):
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+ link = "https://huggingface.co/" + user_id
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+ return f'<a target="_blank" href="{link}">{user_id}</a>'
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+
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+ def get_model_ids():
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+ api = HfApi()
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+ models = api.list_models(filter="deprem-clf-v1")
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+ model_ids = [x.modelId for x in models]
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+ return model_ids
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+
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+ def get_metadata(model_id):
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+ try:
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+ readme_path = hf_hub_download(model_id, filename="README.md")
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+ return metadata_load(readme_path)
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+ except requests.exceptions.HTTPError:
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+ # 404 README.md not found
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+ return None
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+
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+ def parse_metrics_accuracy(meta):
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+ if "model-index" not in meta:
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+ return None
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+ result = meta["model-index"][0]["results"]
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+ metrics = result[0]["metrics"]
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+ accuracy = metrics[2]["value"]
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+ print("Accuracy", accuracy)
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+ return accuracy
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+
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+ def parse_metrics_recall(meta):
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+ if "model-index" not in meta:
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+ return None
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+ result = meta["model-index"][0]["results"]
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+ metrics = result[0]["metrics"]
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+ recall = metrics[0]["value"]
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+ print("Recall", recall)
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+ return recall
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+
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+ def parse_metrics_f1(meta):
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+ if "model-index" not in meta:
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+ return None
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+ result = meta["model-index"][0]["results"]
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+ metrics = result[0]["metrics"]
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+ f1 = metrics[1]["value"]
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+ print("F1-score", f1)
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+ return f1
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+
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+ #@st.cache(ttl=600)
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+ def get_data():
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+ data = []
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+ model_ids = get_model_ids()
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+ for model_id in tqdm(model_ids):
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+ meta = get_metadata(model_id)
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+ if meta is None:
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+ continue
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+ user_id = model_id.split('/')[0]
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+ row = {}
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+ row["User"] = user_id
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+ row["Model"] = model_id
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+ recall = parse_metrics_recall(meta)
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+ row["Recall"] = recall
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+ f1 = parse_metrics_recall(meta)
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+ row["F1-Score"] = f1
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+ data.append(row)
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+ return pd.DataFrame.from_records(data)
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+
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+ dataframe = get_data()
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+ dataframe = dataframe.fillna("")
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+
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+ st.markdown("# Deprem Niyet Analizi için Lider Tablosu")
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+
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+ st.markdown("Bu lider tablosu modellerimizi versiyonladıktan sonra hangi modeli üretime çıkarmamız gerektiğinin takibini yapmak için kullanılır.")
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+ st.markdown(
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+ "Model card'da metadata'da tags kısmına deprem-clf-v1 yazarsanız modeliniz buraya otomatik eklenir."
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+ )
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+ st.markdown(
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+ "Burada recall, f1-score ve accuracy'nin macro average'ına bakıyoruz. Model card'ın metadata kısmında bu üç veriyi log'lamanız yeterli. Burada classification report çıkarırken confidence threshold 0.2."
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+ )
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+ st.markdown("Örnek metadata için [bu model card'ın metadata kısmını](https://huggingface.co/deprem-ml/deprem-roberta-intent/blob/main/README.md) kopyalayıp yapıştırarak kendi metriklerinize göre ayarlayabilirsiniz.")
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+ st.markdown(
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+ "Modelin üstüne tıklayıp model card'a gidebilirsiniz."
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+ )
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+
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+
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+
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+ # turn the model ids into clickable links
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+ dataframe["User"] = dataframe["User"].apply(make_clickable_user)
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+ dataframe["Model"] = dataframe["Model"].apply(make_clickable_model)
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+ dataframe = dataframe.sort_values(by=['F1-Score'], ascending=False)
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+ table_html = dataframe.to_html(escape=False, index=False)
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+ table_html = table_html.replace("<th>", '<th align="left">') # left-align the headers
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+ st.write(table_html, unsafe_allow_html=True)