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import gradio as gr
from functions import example_question_generator, chatbot_with_fc
from data_sources import process_data_upload
from utils import message_dict
import ast
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),gr.update(visible=display)]
with gr.Blocks() as demo:
description = gr.HTML("""
<!-- Header -->
<div class="max-w-4xl mx-auto mb-12 text-center">
<div class="bg-blue-50 border border-blue-200 rounded-lg max-w-2xl mx-auto">
<h2 class="font-semibold text-blue-800 ">
<i class="fas fa-info-circle mr-2"></i>Supported Files
</h2>
<div class="flex flex-wrap justify-center gap-3 pb-4 text-blue-700">
<span class="tooltip">
<i class="fas fa-file-csv mr-1"></i>CSV
<span class="tooltip-text">Comma-separated values</span>
</span>
<span class="tooltip">
<i class="fas fa-file-alt mr-1"></i>TSV
<span class="tooltip-text">Tab-separated values</span>
</span>
<span class="tooltip">
<i class="fas fa-file-alt mr-1"></i>TXT
<span class="tooltip-text">Text files</span>
</span>
<span class="tooltip">
<i class="fas fa-file-excel mr-1"></i>XLS/XLSX
<span class="tooltip-text">Excel spreadsheets</span>
</span>
<span class="tooltip">
<i class="fas fa-file-code mr-1"></i>XML
<span class="tooltip-text">XML documents</span>
</span>
<span class="tooltip">
<i class="fas fa-file-code mr-1"></i>JSON
<span class="tooltip-text">JSON data files</span>
</span>
</div>
</div>
</div>
""")
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 sample-btn bg-gradient-to-r from-purple-500 to-indigo-600 text-white p-6 rounded-lg text-left hover:shadow-lg", size="md", variant="primary")
example_btn_2 = gr.Button(value="Try Me: online_retail_data.csv", elem_classes="example_btn sample-btn bg-gradient-to-r from-purple-500 to-indigo-600 text-white p-6 rounded-lg text-left hover:shadow-lg", 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 drop-zone border-2 border-dashed border-gray-300 rounded-lg hover:border-primary cursor-pointer bg-gray-50 hover:bg-blue-50 transition-colors duration-300", 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, description])
@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?"],
["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,
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
if __name__ == "__main__":
demo.launch() |