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import ast
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import gradio as gr
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from functions import doc_db_example_question_generator, doc_db_chatbot_with_fc
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from data_sources import connect_doc_db
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from utils import message_dict
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def hide_info():
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return gr.update(visible=False)
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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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<p>This tool allows users to communicate with and query real time data from a Document DB (MongoDB for now, others can be added if requested) using natural
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language and the above features.</p>
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<p style="font-weight:bold;">Notice: the way this system is designed, no login information is retained and credentials are passed as session variables until the user leaves or
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refreshes the page in which they disappear. They are never saved to any files. I also make use of the PyMongoArrow aggregate_pandas_all function to apply pipelines,
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which can't delete, drop, or add database lines to avoid unhappy accidents or glitches.
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That being said, it's probably not a good idea to connect a production database to a strange AI tool with an unfamiliar author.
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This should be for demonstration purposes.</p>
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<p>Contact me if this is something you would like built in your organization, on your infrastructure, and with the requisite privacy and control a production
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database analytics tool requires.</p>
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</div>
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</div>
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""", elem_classes="description_component")
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connection_string = gr.Textbox(label="Connection String", value="dataanalyst0.l1klmww.mongodb.net/")
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with gr.Row():
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connection_user = gr.Textbox(label="Connection User", value="virtual-data-analyst")
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connection_password = gr.Textbox(label="Connection Password", value="zcpbmoGJ3mC8o", type="password")
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doc_db_name = gr.Textbox(label="Database Name", value="sample_mflix")
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submit = gr.Button(value="Submit")
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submit.click(fn=hide_info, outputs=description)
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@gr.render(inputs=[connection_string,connection_user,connection_password,doc_db_name], triggers=[submit.click])
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def sql_chat(request: gr.Request, connection_string=connection_string.value, connection_user=connection_user.value, connection_password=connection_password.value, doc_db_name=doc_db_name.value):
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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]['doc_db'] = None
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connection_login_value = "mongodb+srv://" + connection_user + ":" + connection_password + "@" + connection_string
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if connection_login_value:
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print("MONGO APP")
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process_message = process_doc_db(connection_login_value, doc_db_name, 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 "dataanalyst0.l1klmww.mongodb.net" in connection_login_value:
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example_questions = [
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["Describe the dataset"],
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["What are the top 5 most common movie genres?"],
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["How do user comment counts on a movie correlate with the movie award wins?"],
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["Can you generate a pie chart showing the top 10 states with the most movie theaters?"],
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["What are the top 10 most represented directors in the database?"],
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["What are the different movie categories and how many movies are in each category?"]
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]
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else:
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try:
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generated_examples = ast.literal_eval(doc_db_example_question_generator(request.session_hash, process_message[2], doc_db_name, process_message[3]))
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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("DOC DB 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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db_connection_string = gr.Textbox(visible=False, value=connection_login_value)
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db_name = gr.Textbox(visible=False, value=doc_db_name)
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db_collections = gr.Textbox(value=process_message[2], interactive=False, label="DB Collections")
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db_schema = gr.Textbox(visible=False, value=process_message[3])
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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=doc_db_chatbot_with_fc,
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type='messages',
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chatbot=bot,
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title="Chat with your Database",
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examples=example_questions,
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concurrency_limit=None,
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additional_inputs=[session_hash, db_connection_string, db_name, db_collections,db_schema]
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)
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def process_doc_db(connection_string, nosql_db_name, session_hash):
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if connection_string:
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process_message = connect_doc_db(connection_string, nosql_db_name, session_hash)
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return process_message
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if __name__ == "__main__":
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demo.launch() |
