app.py

import gradio as gr
from pandasai_tool import pandas_ai_res
import time

# import csv or xlsx file and use input as df
# pandas_ai_res function will return resultr
with gr.Blocks() as demo:
    gr.Label("🗣말로 하는 데이터 분석")
    gr.Markdown("(안내) 데이터 파일을 업로드하고, 분석을 부탁하세요")
    # uploaded_file = gr.File(label="파일 업로드")
    # chatbot = gr.Chatbot()
    # msg = gr.Textbox()
    # clear = gr.ClearButton([msg, chatbot], label="채팅 기록 삭제")
    #
    # def respond(file, message, chat_history):
    #     bot_message = pandas_ai_res(file, message)
    #     chat_history.append((message, bot_message))
    #     # time.sleep(2)
    #     return "", chat_history
    #
    # msg.submit(respond, [uploaded_file, msg, chatbot], [msg, chatbot])

    gr.Interface(
        fn=pandas_ai_res,
        inputs=["file", "text"],
        outputs="text"
    )

if __name__ == "__main__":
    demo.launch(share=True)

#
# import gradio as gr
# import pandas as pd
# from huggingface_hub.hf_api import create_repo, upload_file, HfApi
# from huggingface_hub.repository import Repository
# import subprocess
# import os
# import tempfile
#
#
# import sweetviz as sv
#
#
# def analyze_datasets(dataset, dataset_name, token, column=None, pairwise="off"):
#     df = pd.read_csv(dataset.name)
#     username = HfApi().whoami(token=token)["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}"
#
#
#
#
#
#
# with gr.Blocks() as demo:
#     main_title = gr.Markdown("""# Easy Analysis🪄🌟✨""")
#     main_desc = gr.Markdown(
#         """This app enables you to run three type of dataset analysis and pushes the interactive reports to your Hugging Face Hub profile as a Space. It uses SweetViz in the back.""")
#     with gr.Tabs():
#         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")
#                     dataset_name = gr.Textbox(label="Dataset Name")
#                     pushing_desc = gr.Markdown(
#                         "This app needs your Hugging Face Hub 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, token, column, pairwise],
#                     outputs=outcome,
#                     status_tracker=inference_progress,
#                 )
#         with gr.TabItem("Compare Splits") as compare_splits:
#             with gr.Row():
#                 with gr.Column():
#                     title = gr.Markdown(""" ## Compare Splits""")
#                     description = gr.Markdown(
#                         "Split a dataset and compare splits. You need to give a fraction, e.g. 0.8.")
#                     dataset = gr.File(label="Dataset")
#                     split_ratio = gr.Number(label="Split Ratios")
#                     pushing_desc = gr.Markdown(
#                         "This app needs your Hugging Face Hub token and a unique name for your dataset report.")
#                     token = gr.Textbox(label="Your Hugging Face Token")
#                     dataset_name = gr.Textbox(label="Dataset Name")
#                     inference_run = gr.Button("Infer")
#                     inference_progress = gr.StatusTracker(cover_container=True)
#
#                 outcome = gr.outputs.Textbox()
#                 inference_run.click(
#                     compare_dataset_splits,
#                     inputs=[dataset, dataset_name, token, split_ratio],
#                     outputs=outcome,
#                     status_tracker=inference_progress,
#                 )
#
#         with gr.TabItem("Compare Subsets") as compare_subsets:
#             with gr.Row():
#                 with gr.Column():
#                     title = gr.Markdown(""" ## Compare Subsets""")
#                     description = gr.Markdown(
#                         "Compare subsets of a dataset, e.g. you can pick Age Group column and compare adult category against young.")
#                     dataset = gr.File(label="Dataset")
#                     column = gr.Text(label="Enter column:")
#                     category = gr.Text(label="Enter category:")
#                     pushing_desc = gr.Markdown(
#                         "This app needs your Hugging Face Hub token and a unique name for your dataset report.")
#                     token = gr.Textbox(label="Your Hugging Face Token")
#                     dataset_name = gr.Textbox(label="Dataset Name")
#                     inference_run = gr.Button("Run Analysis")
#                     inference_progress = gr.StatusTracker(cover_container=True)
#
#                 outcome = gr.outputs.Textbox()
#                 inference_run.click(
#                     compare_column_values,
#                     inputs=[dataset, dataset_name, token, column, category],
#                     outputs=outcome,
#                     status_tracker=inference_progress,
#                 )