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import gradio as gr |
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import os |
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import re |
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from dotenv import load_dotenv |
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from contextlib import redirect_stdout |
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from io import StringIO |
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from langchain import SQLDatabase, SQLDatabaseChain |
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from langchain.llms import AzureOpenAI |
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from langchain.agents import create_sql_agent |
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from langchain.agents.agent_toolkits import SQLDatabaseToolkit |
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from langchain.agents.agent_types import AgentType |
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load_dotenv(os.getcwd() + "/.env") |
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llm = AzureOpenAI( |
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model_name=os.environ["OPENAI_MODEL_NAME"], |
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deployment_name=os.environ["OPENAI_DEPLOYMENT_NAME"], |
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temperature=0, |
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) |
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sqlite_db_path = "data/Chinook.db" |
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db = SQLDatabase.from_uri(f"sqlite:///{sqlite_db_path}") |
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db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True) |
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agent_executor = create_sql_agent( |
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llm=llm, |
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toolkit=SQLDatabaseToolkit(db=db, llm=llm), |
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verbose=True, |
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agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, |
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) |
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def clear_input(): |
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return "", "Hit 'Submit' to see output here" |
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def generate_output_of_db_chain(user_message): |
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print(user_message) |
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if not user_message: |
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print("Empty input") |
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yield "Please enter a messager before hitting Send!" |
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with redirect_stdout(StringIO()) as f: |
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db_chain.run(user_message) |
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s = f.getvalue() |
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s = s[6:].replace('\n', '<br/>') |
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yield re.sub(r"(\x1b)?\[(\d+[m;])+", "", s) |
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def generate_output_of_db_agent(user_message): |
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if not user_message: |
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print("Empty input") |
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yield "Please enter a messager before hitting Send!" |
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return "" |
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with redirect_stdout(StringIO()) as f: |
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agent_executor.run(user_message) |
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s = f.getvalue() |
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s = s[6:].replace("\n", "<br/>") |
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yield re.sub(r"(\x1b)?\[(\d+[m;])+", "", s) |
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custom_css = """ |
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#banner-image { |
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display: block; |
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margin-left: auto; |
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margin-right: auto; |
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} |
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#chat-message { |
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font-size: 14px; |
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min-height: 300px; |
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} |
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""" |
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with gr.Blocks(analytics_enabled=False, css=custom_css) as demo: |
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gr.HTML("""<h1 align="center">LLM Mini-Series #4 π¬</h1>""") |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown( |
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f""" |
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π» TODO Add some nice description text |
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""" |
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) |
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gr.HTML("""<h2 align="left">Using LangChain's SQLDatabaseChain</h2>""") |
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with gr.Row(): |
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with gr.Column(): |
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user_message = gr.Textbox( |
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placeholder="Enter your message here", |
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show_label=False, |
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elem_id="q-input", |
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) |
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with gr.Row(): |
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clear_btn = gr.Button("Clear", elem_id="clear-btn", visible=True) |
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submit_btn = gr.Button("Submit", elem_id="submit-btn", visible=True) |
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with gr.Box(): |
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output_field = gr.HTML( |
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value="Hit 'Submit' to see output here", |
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label="Output of model", |
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interactive=False, |
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) |
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gr.HTML("""<h2 align="left">Using an agent-based approach with LangChain""") |
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with gr.Row(): |
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with gr.Column(): |
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user_message_agent = gr.Textbox( |
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placeholder="Enter your message here", |
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show_label=False, |
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elem_id="q-agent-input", |
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) |
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with gr.Row(): |
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clear_agent_btn = gr.Button( |
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"Clear", elem_id="clear-agent-btn", visible=True |
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) |
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submit_agent_btn = gr.Button( |
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"Submit", elem_id="submit-agent-btn", visible=True |
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) |
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with gr.Box(): |
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output_agent_field = gr.HTML( |
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value="Hit 'Submit' to see output here", |
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label="Output of model", |
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interactive=False, |
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) |
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clear_btn.click(clear_input, outputs=[user_message, output_field]) |
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submit_btn.click( |
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generate_output_of_db_chain, inputs=[user_message], outputs=[output_field] |
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) |
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submit_agent_btn.click( |
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generate_output_of_db_agent, |
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inputs=[user_message_agent], |
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outputs=[output_agent_field], |
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) |
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clear_agent_btn.click(clear_input, outputs=[user_message_agent, output_agent_field]) |
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demo.queue(concurrency_count=16).launch(debug=True) |
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