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import gradio as gr
import pandas as pd
from huggingface_hub.hf_api import create_repo, upload_folder, upload_file
from huggingface_hub.repository import Repository
import subprocess
import os
import tempfile
from uuid import uuid4
import pickle
import sweetviz as sv
import dabl
import re
def analyze_datasets(dataset, dataset_name, username, token, column=None, pairwise="off"):
df = pd.read_csv(dataset.name)
if column is not None:
analyze_report = sv.analyze(df, target_feat=column, pairwise_analysis=pairwise)
else:
analyze_report = sv.analyze(df, pairwise_analysis=pairwise)
analyze_report.show_html('index.html', open_browser=False)
repo_url = create_repo(f"{username}/{dataset_name}", repo_type = "space", token = token, space_sdk = "static", private=False)
upload_file(path_or_fileobj ="./index.html", path_in_repo = "index.html", repo_id =f"{username}/{dataset_name}", repo_type = "space", token=token)
readme = f"---\ntitle: {dataset_name}\nemoji: ✨\ncolorFrom: green\ncolorTo: red\nsdk: static\npinned: false\ntags:\n- dataset-report\n---"
with open("README.md", "w+") as f:
f.write(readme)
upload_file(path_or_fileobj ="./README.md", path_in_repo = "README.md", repo_id =f"{username}/{dataset_name}", repo_type = "space", token=token)
return f"Your dataset report will be ready at {repo_url}"
from sklearn.utils import estimator_html_repr
def extract_estimator_config(model):
hyperparameter_dict = model.get_params(deep=True)
table = "| Hyperparameters | Value |\n| :-- | :-- |\n"
for hyperparameter, value in hyperparameter_dict.items():
table += f"| {hyperparameter} | {value} |\n"
return table
def train_baseline(dataset, username, dataset_name, token, column):
df = pd.read_csv(dataset.name)
fc = dabl.SimpleClassifier(random_state=0)
df_clean = dabl.clean(df)
X = df_clean.drop(column, axis = 1)
y = df_clean[column]
with tempfile.TemporaryDirectory() as tmpdirname:
from contextlib import redirect_stdout
with open('logs.txt', 'w') as f:
with redirect_stdout(f):
print('Logging training')
fc.fit(X, y)
repo_url = create_repo(repo_id = f"{username}/{dataset_name}", token = token)
readme = f"---\nlicense: apache-2.0\nlibrary_name: sklearn\n---\n\n"
readme += f"## Baseline Model trained on {dataset_name} to predict {column}\n\n"
readme+="Metrics of the best model:\n\n"
for elem in str(fc.current_best_).split("\n"):
readme+= f"{elem}\n\n"
readme+= "\n\nSee model plot below:\n\n"
readme+= re.sub(r"\n\s+", "", str(estimator_html_repr(fc.est_)))
with open(f"{tmpdirname}/README.md", "w+") as f:
f.write(readme)
with open(f"{tmpdirname}/clf.pkl", mode="bw") as f:
pickle.dump(fc, file=f)
upload_folder(repo_id =f"{username}/{dataset_name}", folder_path=tmpdirname, repo_type = "model", token=token, path_in_repo="./")
return f"Your model will be ready at {repo_url}"
with gr.Blocks() as demo:
main_title = gr.Markdown("""# Baseline Trainer 🪄🌟✨""")
main_desc = gr.Markdown("""This app trains a baseline model for a given dataset and pushes it to your Hugging Face Hub Profile with a model card.""")
with gr.Tabs():
with gr.TabItem("Baseline Trainer") as baseline_trainer:
with gr.Row():
with gr.Column():
title = gr.Markdown(""" ## Train a supervised baseline model""")
description = gr.Markdown("This app trains a model and pushes it to your Hugging Face Hub Profile.")
dataset = gr.File(label = "Dataset")
column = gr.Text(label = "Enter target variable:")
dataset_name = gr.Text(label = "Enter dataset name:")
pushing_desc = gr.Markdown("This app needs your Hugging Face Hub user name, token and a unique name for your dataset report.")
token = gr.Textbox(label = "Your Hugging Face Token")
username = gr.Textbox(label = "Your Hugging Face User Name")
inference_run = gr.Button("Train")
inference_progress = gr.StatusTracker(cover_container=True)
outcome = gr.outputs.Textbox(label = "Progress")
inference_run.click(
train_baseline,
inputs=[dataset, username, dataset_name, token, column],
outputs=outcome,
status_tracker=inference_progress,
)
with gr.TabItem("Analyze") as analyze:
with gr.Row():
with gr.Column():
title = gr.Markdown(""" ## Analyze Dataset """)
description = gr.Markdown("Analyze a dataset or predictive variables against a target variable in a dataset (enter a column name to column section if you want to compare against target value). You can also do pairwise analysis, but it has quadratic complexity.")
dataset = gr.File(label = "Dataset")
column = gr.Text(label = "Compare dataset against a target variable (Optional)")
pairwise = gr.Radio(["off", "on"], label = "Enable pairwise analysis")
token = gr.Textbox(label = "Your Hugging Face Token")
username = gr.Textbox(label = "Your Hugging Face User Name")
dataset_name = gr.Textbox(label = "Dataset Name")
pushing_desc = gr.Markdown("This app needs your Hugging Face Hub user name, token and a unique name for your dataset report.")
inference_run = gr.Button("Infer")
inference_progress = gr.StatusTracker(cover_container=True)
outcome = gr.outputs.Textbox()
inference_run.click(
analyze_datasets,
inputs=[dataset, dataset_name, username, token, column, pairwise],
outputs=outcome,
status_tracker=inference_progress,
)
demo.launch(debug=True) |