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from data_sources import process_data_upload
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from functions import example_question_generator, chatbot_with_fc
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from utils import TEMP_DIR, message_dict
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import gradio as gr
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import ast
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import os
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from getpass import getpass
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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:")
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def delete_db(req: gr.Request):
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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
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def run_example(input):
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return input
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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)]
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css= ".file_marker .large{min-height:50px !important;} .example_btn{max-width:300px;} .padding{padding:0;}"
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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>")
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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>
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<div style="margin:auto;max-width: 500px;">
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<p style="margin:0;font-style:italic;">Currently accepts CSV, TSV, TXT, XLS, XLSX, XML, and JSON files.</p>
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<p style="margin:0;font-style:italic;">Can run SQL queries, linear regressions, and analyze the results.</p>
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<p style="margin:0;font-style:italic;">Can generate scatter plots, line charts, pie charts, bar graphs, histograms, time series, and more.
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New visualizations types added regularly.</p>
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</div>
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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>""")
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example_file_1 = gr.File(visible=False, value="samples/bank_marketing_campaign.csv")
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example_file_2 = gr.File(visible=False, value="samples/online_retail_data.csv")
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with gr.Row():
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example_btn_1 = gr.Button(value="Try Me: bank_marketing_campaign.csv", elem_classes="example_btn", size="md", variant="primary")
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example_btn_2 = gr.Button(value="Try Me: online_retail_data.csv", elem_classes="example_btn", size="md", variant="primary")
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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'])
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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])
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@gr.render(inputs=file_output)
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def data_options(filename, request: gr.Request):
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print(filename)
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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:
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example_questions = [
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["Describe the dataset"],
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["What levels of education have the highest and lowest average balance?"],
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["What job is most and least common for a yes response from the individuals, not counting 'unknown'?"],
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["Can you generate a bar chart of education vs. average balance?"],
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["Can you generate a table of levels of education versus average balance, percent married, percent with a loan, and percent in default?"],
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["Can we predict the relationship between the number of contacts performed before this campaign and the average balance?"],
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]
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elif "online_retail_data" in filename:
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example_questions = [
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["Describe the dataset"],
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["What month had the highest revenue?"],
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["Is revenue higher in the morning or afternoon?"],
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["Can you generate a line graph of revenue per month?"],
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["Can you generate a table of revenue per month?"],
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["Can we predict how time of day affects revenue in this data set?"],
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]
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else:
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try:
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generated_examples = ast.literal_eval(example_question_generator(request.session_hash))
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example_questions = [
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["Describe the dataset"]
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]
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for example in generated_examples:
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example_questions.append([example])
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except:
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example_questions = [
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["Describe the dataset"],
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["List the columns in the dataset"],
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["What could this data be used for?"],
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]
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parameters = gr.Textbox(visible=False, value=request.session_hash)
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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")
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chat = gr.ChatInterface(
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fn=chatbot_with_fc,
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type='messages',
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chatbot=bot,
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title="Chat with your data file",
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concurrency_limit=None,
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examples=example_questions,
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additional_inputs=parameters
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)
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def process_upload(upload_value, session_hash):
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if upload_value:
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process_message = process_data_upload(upload_value, session_hash)
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return process_message
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demo.unload(delete_db)
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demo.launch(debug=True, allowed_paths=["temp/"]) |
