| from data_sources import process_data_upload
|
| from functions import example_question_generator, chatbot_with_fc
|
| from utils import TEMP_DIR, message_dict
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| import gradio as gr
|
|
|
| import ast
|
| import os
|
| from getpass import getpass
|
| from dotenv import load_dotenv
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|
|
| load_dotenv()
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|
|
| if "OPENAI_API_KEY" not in os.environ:
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| os.environ["OPENAI_API_KEY"] = getpass("Enter OpenAI API key:")
|
|
|
| def delete_db(req: gr.Request):
|
| import shutil
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| dir_path = TEMP_DIR / str(req.session_hash)
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| if os.path.exists(dir_path):
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| shutil.rmtree(dir_path)
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| message_dict[req.session_hash] = None
|
|
|
| def run_example(input):
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| return input
|
|
|
| def example_display(input):
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| if input == None:
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| display = True
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| else:
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| display = False
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| return [gr.update(visible=display),gr.update(visible=display)]
|
|
|
| css= ".file_marker .large{min-height:50px !important;} .example_btn{max-width:300px;} .padding{padding:0;}"
|
|
|
| with gr.Blocks(css=css, delete_cache=(3600,3600)) as demo:
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| title = gr.HTML("<h1 style='text-align:center;'>Virtual Data Analyst</h1>")
|
| description = gr.HTML("""<p style='text-align:center;'>A helpful tool for data analysis, visualizations, regressions, and more.
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| Upload a data file and chat with our virtual data analyst to get insights on your data set.
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| Try a sample file to get started!</p>
|
| <ul style="margin:auto;max-width: 500px;">
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| <li style="margin:0;line-height:1;">Currently accepts CSV, TSV, TXT, XLS, XLSX, XML, and JSON files.</li>
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| <li style="margin:0;line-height:1;">Can run SQL queries, linear regressions, and analyze the results.</li>
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| <li style="margin:0;line-height:1;">Can generate scatter plots, line charts, pie charts, bar graphs, histograms, time series, and more.
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| New visualizations types added regularly.</li>
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| </ul>
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| <p style='text-align:center;'>This application is under active development. If you experience bugs with use,
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| open a discussion in the community tab and I will respond.</p>""")
|
| example_file_1 = gr.File(visible=False, value="samples/bank_marketing_campaign.csv")
|
| example_file_2 = gr.File(visible=False, value="samples/online_retail_data.csv")
|
| with gr.Row():
|
| example_btn_1 = gr.Button(value="Try Me: bank_marketing_campaign.csv", elem_classes="example_btn", size="md", variant="primary")
|
| example_btn_2 = gr.Button(value="Try Me: online_retail_data.csv", elem_classes="example_btn", size="md", variant="primary")
|
|
|
| file_output = gr.File(label="Data File (CSV, TSV, TXT, XLS, XLSX, XML, JSON)", show_label=True, elem_classes="file_marker", file_types=['.csv','.xlsx','.txt','.json','.ndjson','.xml','.xls','.tsv'])
|
| example_btn_1.click(fn=run_example, inputs=example_file_1, outputs=file_output)
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| example_btn_2.click(fn=run_example, inputs=example_file_2, outputs=file_output)
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| file_output.change(fn=example_display, inputs=file_output, outputs=[example_btn_1, example_btn_2])
|
|
|
| @gr.render(inputs=file_output)
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| def data_options(filename, request: gr.Request):
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| print(filename)
|
| message_dict[request.session_hash] = None
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| if filename:
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| process_message = process_upload(filename, request.session_hash)
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| gr.HTML(value=process_message[1], padding=False)
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| if process_message[0] == "success":
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| if "bank_marketing_campaign" in filename:
|
| example_questions = [
|
| ["Describe the dataset"],
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| ["What levels of education have the highest and lowest average balance?"],
|
| ["What job is most and least common for a yes response from the individuals, not counting 'unknown'?"],
|
| ["Can you generate a bar chart of education vs. average balance?"],
|
| ["Can you generate a table of levels of education versus average balance, percent married, percent with a loan, and percent in default?"],
|
| ["Can we predict the relationship between the number of contacts performed before this campaign and the average balance?"],
|
| ["Can you plot the number of contacts performed before this campaign versus the duration and use balance as the size in a bubble chart?"]
|
| ]
|
| elif "online_retail_data" in filename:
|
| example_questions = [
|
| ["Describe the dataset"],
|
| ["What month had the highest revenue?"],
|
| ["Is revenue higher in the morning or afternoon?"],
|
| ["Can you generate a line graph of revenue per month?"],
|
| ["Can you generate a table of revenue per month?"],
|
| ["Can we predict how time of day affects transaction value in this data set?"],
|
| ["Can you plot revenue per month with size being the number of units sold that month in a bubble chart?"]
|
| ]
|
| else:
|
| try:
|
| generated_examples = ast.literal_eval(example_question_generator(request.session_hash))
|
| example_questions = [
|
| ["Describe the dataset"]
|
| ]
|
| for example in generated_examples:
|
| example_questions.append([example])
|
| except:
|
| example_questions = [
|
| ["Describe the dataset"],
|
| ["List the columns in the dataset"],
|
| ["What could this data be used for?"],
|
| ]
|
| parameters = gr.Textbox(visible=False, value=request.session_hash)
|
| bot = gr.Chatbot(type='messages', label="CSV Chat Window", render_markdown=True, sanitize_html=False, show_label=True, render=False, visible=True, elem_classes="chatbot")
|
| chat = gr.ChatInterface(
|
| fn=chatbot_with_fc,
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| type='messages',
|
| chatbot=bot,
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| title="Chat with your data file",
|
| concurrency_limit=None,
|
| examples=example_questions,
|
| additional_inputs=parameters
|
| )
|
|
|
| def process_upload(upload_value, session_hash):
|
| if upload_value:
|
| process_message = process_data_upload(upload_value, session_hash)
|
| return process_message
|
|
|
| demo.unload(delete_db)
|
|
|
|
|
| demo.launch(debug=True, allowed_paths=["temp/"]) |
