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import ast
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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 connect_graphql
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from utils import message_dict
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import os
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from dotenv import load_dotenv
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load_dotenv()
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graphql_sample_endpoint = os.getenv("GRAPHQL_SAMPLE_ENDPOINT")
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graphql_sample_api_token = os.getenv("GRAPHQL_SAMPLE_API_TOKEN")
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graphql_sample_header_name = os.getenv("GRAPHQL_SAMPLE_HEADER_NAME")
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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 GraphQL API endpoint using natural
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language and the above features.</p>
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<p style="font-weight:bold;">Notice: API querying is the most difficult and experimental feature so far.
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This tool may have variable performance and quality, although it should get better over time as I evaluate use.
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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.</p>
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<p style="font-weight:bold;"> I don't include a function that allows the system to run mutations and I instruct the agent to not alter any data, but it could in theory be possible,
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although my testing wasn't able to get the system to alter or write to the api. I would be careful to make sure permissions are restricted for the
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api token being used.
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And of course, it's probably best to use caution when connecting 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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graphql_url = gr.Textbox(label="GraphQL Endpoint URL", value=graphql_sample_endpoint)
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with gr.Row():
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api_token_header_name = gr.Textbox(label="API Token Header Name", value=graphql_sample_header_name)
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api_token = gr.Textbox(label="API Token", value=graphql_sample_api_token, type="password")
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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=[graphql_url,api_token,api_token_header_name], triggers=[submit.click])
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def api_chat(request: gr.Request, graphql_url=graphql_url.value, api_token=api_token.value, api_token_header_name=api_token_header_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]['graphql'] = None
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if graphql_url:
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print("GraphQL API")
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process_message = process_graphql(graphql_url, api_token, api_token_header_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 "qdl-app-testing" in graphql_url:
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example_questions = [
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["Describe the dataset"],
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["What is the total revenue for this shopify store?"],
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["What is the average duration from the fulfillment of an order to its delivery?"],
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["What is the total value of orders processed in the current month?"],
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["Which product has the highest number of variants in the inventory?"],
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["How many gift cards have been issued this year, and what is their total value?"],
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["How many active apps are currently installed on the store?"],
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["What is the total count of abandoned checkouts over the last month?"]
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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, 'graphql', graphql_url, process_message[2], ''))
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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("GRAPHQL 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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graphql_api_string = gr.Textbox(visible=False, value=graphql_url)
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graphql_api_token = gr.Textbox(visible=False, value=api_token)
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graphql_token_header = gr.Textbox(visible=False, value=api_token_header_name)
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titles = gr.Textbox(value=process_message[2], interactive=False, label="GraphQL Types")
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data_source = gr.Textbox(visible=False, value='graphql')
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schema = gr.Textbox(visible=False, value='')
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bot = gr.Chatbot(type='messages', label="GraphQL 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 Graphql API",
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examples=example_questions,
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concurrency_limit=None,
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additional_inputs=[session_hash, data_source, titles, schema, graphql_api_string, graphql_api_token, graphql_token_header]
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)
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def process_graphql(graphql_url, api_token, api_token_header_name, session_hash):
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if graphql_url:
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process_message = connect_graphql(graphql_url, api_token, api_token_header_name, session_hash)
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return process_message
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if __name__ == "__main__":
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demo.launch() |
