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from data_sources import process_data_upload
from functions import example_question_generator, chatbot_with_fc
from utils import TEMP_DIR, message_dict
import gradio as gr
import ast
import os
from getpass import getpass
from dotenv import load_dotenv
load_dotenv()
if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass("Enter OpenAI API key:")
def delete_db(req: gr.Request):
import shutil
dir_path = TEMP_DIR / str(req.session_hash)
if os.path.exists(dir_path):
shutil.rmtree(dir_path)
message_dict[req.session_hash] = None
def run_example(input):
return input
def example_display(input):
if input == None:
display = True
else:
display = False
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:
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.
Upload a data file and chat with our virtual data analyst to get insights on your data set.
Try a sample file to get started!</p>
<div style="margin:auto;max-width: 500px;">
<p style="margin:0;font-style:italic;">Currently accepts CSV, TSV, TXT, XLS, XLSX, XML, and JSON files.</p>
<p style="margin:0;font-style:italic;">Can run SQL queries, linear regressions, and analyze the results.</p>
<p style="margin:0;font-style:italic;">Can generate scatter plots, line charts, pie charts, bar graphs, histograms, time series, and more.
New visualizations types added regularly.</p>
</div>
<p style='text-align:center;'>This application is under active development. If you experience bugs with use,
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)
example_btn_2.click(fn=run_example, inputs=example_file_2, outputs=file_output)
file_output.change(fn=example_display, inputs=file_output, outputs=[example_btn_1, example_btn_2])
@gr.render(inputs=file_output)
def data_options(filename, request: gr.Request):
print(filename)
message_dict[request.session_hash] = None
if filename:
process_message = process_upload(filename, request.session_hash)
gr.HTML(value=process_message[1], padding=False)
if process_message[0] == "success":
if "bank_marketing_campaign" in filename:
example_questions = [
["Describe the dataset"],
["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?"],
]
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 revenue in this data set?"],
]
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,
type='messages',
chatbot=bot,
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)
## Uncomment the line below to launch the chat app with UI
demo.launch(debug=True, allowed_paths=["temp/"]) |