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import gradio as gr
import pandas as pd
block = gr.Blocks()
NUM_DATASETS = 7
NUM_SCORES = 0
NUM_MODELS = 5
def general_dataframe_update():
"""
Returns general dataframe for general table.
"""
dataframe = pd.read_csv('data/general.csv')
return dataframe
def classification_dataframe_update():
"""
Returns classification dataframe for classification table.
"""
dataframe = pd.read_csv('data/classification.csv')
return dataframe
def sts_dataframe_udpate():
"""
Returns sts dataframe for sts table.
"""
dataframe = pd.read_csv('data/sts.csv')
return dataframe
with block:
gr.Markdown(f"""**Leaderboard de modelos de Embeddings en español
Massive Text Embedding Benchmark (MTEB) Leaderboard.**
- **Total Datasets**: {NUM_DATASETS}
- **Total Languages**: 1
- **Total Scores**: {NUM_SCORES}
- **Total Models**: {NUM_MODELS}
""")
with gr.Tabs():
with gr.TabItem("Overall"):
with gr.Row():
gr.Markdown("""
**Tabla General de Embeddings**
- **Metricas:** Varias, con sus respectivas medias.
- **Idioma:** Español
""")
with gr.Row():
overall = general_dataframe_update()
data_overall = gr.components.Dataframe(
overall,
type="pandas",
wrap=True,
)
with gr.TabItem("Classification"):
with gr.Row():
# Create and display a sample DataFrame
classification = classification_dataframe_update()
data_overall = gr.components.Dataframe(
classification,
type="pandas",
wrap=True,
)
with gr.TabItem("STS"):
with gr.Row():
# Create and display a sample DataFrame
sts = sts_dataframe_udpate()
data_overall = gr.components.Dataframe(
sts,
type="pandas",
wrap=True,
)
block.launch()
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