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import gradio as gr |
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from functions import example_question_generator, chatbot_func |
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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, 'file_upload', '', process_message[1], '')) |
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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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session_hash = gr.Textbox(visible=False, value=request.session_hash) |
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data_source = gr.Textbox(visible=False, value='file_upload') |
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schema = gr.Textbox(visible=False, value='') |
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titles = gr.Textbox(value=process_message[2], interactive=False, visible=False) |
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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_func, |
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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=[session_hash, data_source, titles, schema] |
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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() |
