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import gradio as gr
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from functions import example_question_generator, chatbot_with_fc
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from data_sources import process_data_upload
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from utils import message_dict
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import ast
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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),gr.update(visible=display),gr.update(visible=display)]
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with gr.Blocks() as demo:
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description = gr.HTML("""
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<!-- Header -->
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<div class="max-w-4xl mx-auto mb-12 text-center">
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<div class="bg-blue-50 border border-blue-200 rounded-lg max-w-2xl mx-auto">
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<h2 class="font-semibold text-blue-800 ">
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<i class="fas fa-info-circle mr-2"></i>Supported Files
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</h2>
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<div class="flex flex-wrap justify-center gap-3 pb-4 text-blue-700">
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<span class="tooltip">
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<i class="fas fa-file-csv mr-1"></i>CSV
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<span class="tooltip-text">Comma-separated values</span>
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</span>
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<span class="tooltip">
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<i class="fas fa-file-alt mr-1"></i>TSV
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<span class="tooltip-text">Tab-separated values</span>
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</span>
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<span class="tooltip">
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<i class="fas fa-file-alt mr-1"></i>TXT
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<span class="tooltip-text">Text files</span>
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</span>
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<span class="tooltip">
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<i class="fas fa-file-excel mr-1"></i>XLS/XLSX
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<span class="tooltip-text">Excel spreadsheets</span>
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</span>
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<span class="tooltip">
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<i class="fas fa-file-code mr-1"></i>XML
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<span class="tooltip-text">XML documents</span>
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</span>
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<span class="tooltip">
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<i class="fas fa-file-code mr-1"></i>JSON
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<span class="tooltip-text">JSON data files</span>
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</span>
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</div>
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</div>
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</div>
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""", elem_classes="description_component")
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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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example_file_3 = gr.File(visible=False, value="samples/tb_illness_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="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")
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example_btn_2 = gr.Button(value="Try Me: online_retail_data.csv", elem_classes="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")
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example_btn_3 = gr.Button(value="Try Me: tb_illness_data.csv", elem_classes="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")
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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 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'])
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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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example_btn_3.click(fn=run_example, inputs=example_file_3, outputs=file_output)
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file_output.change(fn=example_display, inputs=file_output, outputs=[example_btn_1, example_btn_2, example_btn_3, description])
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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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if request.session_hash not in message_dict:
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message_dict[request.session_hash] = {}
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message_dict[request.session_hash]['file_upload'] = 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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["Can you plot the number of contacts performed before this campaign versus the duration and use balance as the size in a bubble chart?"]
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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 transaction value in this data set?"],
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["Can you plot revenue per month with size being the number of units sold that month in a bubble chart?"]
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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 Exception as e:
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print("DATA FILE QUESTION GENERATION ERROR")
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print(e)
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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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if __name__ == "__main__":
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demo.launch() |
