test.py

import gradio as gr

def bot(input_message: str, db_info="", temperature=0.1, top_p=0.9, top_k=0, repetition_penalty=1.08, format_sql=True, stop_sequence="Explanation,Note", log=True):
    # For the stripped down version, let's just return a preset output
    final_query = "| stadium : stadium_id , location , name , capacity , highest , lowest , average | singer : singer_id , name , country , song_name , song_release_year , age , is_male | concert : concert_id , concert_name , theme , stadium_id , year | singer_in_concert : concert_id , singer_id | concert.stadium_id = stadium.stadium_id | singer_in_concert.singer_id = singer.singer_id | singer_in_concert.concert_id = concert.concert_id |" 
    final_query_markdown = f"{final_query}"
    return final_query_markdown

# Gradio UI Code
with gr.Blocks(theme='gradio/soft') as demo:
    # Elements stack vertically by default just define elements in order you want them to stack
    header = gr.HTML("""
        <h1 style="text-align: center">SQL Skeleton WizardCoder Demo</h1>
        <h3 style="text-align: center">🕷️☠️🧙‍♂️ Generate SQL queries from Natural Language 🕷️☠️🧙‍♂️</h3>
    """)

    output_box = gr.Code(label="Generated SQL", lines=2, interactive=True)
    note = gr.HTML("""<p style="font-size: 12px; text-align: center">⚠️ Should take 30-60s to generate</p>""")
    input_text = gr.Textbox(lines=3, placeholder='Write your question here...', label='NL Input')
    db_info = gr.Textbox(lines=4, placeholder='Example: | table_01 : column_01 , column_02 | table_02 : column_01 , column_02 | ...', label='Database Info')
    format_sql = gr.Checkbox(label="Format SQL + Remove Skeleton", value=True, interactive=True)
    
    # Generate button UI element
    run_button = gr.Button("Generate SQL", variant="primary")

    with gr.Accordion("Options", open=False):
        temperature = gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.2, step=0.1)
        top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.0, maximum=1.0, value=0.9, step=0.01)
        top_k = gr.Slider(label="Top-k", minimum=0, maximum=200, value=0, step=1)
        repetition_penalty = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.08, step=0.01)
        stop_sequence = gr.Textbox(lines=1, value="Explanation,Note", label='Extra Stop Sequence')
    
    ## Add statement saying that inputs/outpus are sent to firebase
    info = gr.HTML(f"""
        <p>🌐 Leveraging the <a href='https://huggingface.co/{quantized_model}'><strong>4-bit GGML version</strong></a> of <a href='https://huggingface.co/{merged_model}'><strong>{merged_model}</strong></a> model.</p>
        <p>🔗 How it's made: <a href='https://huggingface.co/{initial_model}'><strong>{initial_model}</strong></a> was finetuned to create <a href='https://huggingface.co/{lora_model}'><strong>{lora_model}</strong></a>, then merged together to create <a href='https://huggingface.co/{merged_model}'><strong>{merged_model}</strong></a>.</p>
        <p>📉 Fine-tuning was performed using QLoRA techniques on the <a href='https://huggingface.co/datasets/{dataset}'><strong>{dataset}</strong></a> dataset. You can view training metrics on the <a href='https://huggingface.co/{lora_model}'><strong>QLoRa adapter HF Repo</strong></a>.</p>
        <p>📊 All inputs/outputs are logged to Firebase to see how the model is doing.</a></p>
    """)

    examples = gr.Examples([
        ["What is the average, minimum, and maximum age of all singers from France?", "| stadium : stadium_id , location , name , capacity , highest , lowest , average | singer : singer_id , name , country , song_name , song_release_year , age , is_male | concert : concert_id , concert_name , theme , stadium_id , year | singer_in_concert : concert_id , singer_id | concert.stadium_id = stadium.stadium_id | singer_in_concert.singer_id = singer.singer_id | singer_in_concert.concert_id = concert.concert_id |"],
        ["How many students have dogs?", "| student : stuid , lname , fname , age , sex , major , advisor , city_code | has_pet : stuid , petid | pets : petid , pettype , pet_age , weight | has_pet.stuid = student.stuid | has_pet.petid = pets.petid | pets.pettype = 'Dog' |"],
    ], inputs=[input_text, db_info, temperature, top_p, top_k, repetition_penalty, format_sql, stop_sequence], fn=generate, cache_examples=False if platform.system() == "Windows" or platform.system() == "Darwin" else True, outputs=output_box)

    with gr.Accordion("More Examples", open=False):
        examples = gr.Examples([
            ["What is the average weight of pets of all students?", "| student : stuid , lname , fname , age , sex , major , advisor , city_code | has_pet : stuid , petid | pets : petid , pettype , pet_age , weight | has_pet.stuid = student.stuid | has_pet.petid = pets.petid |"],
            ["How many male singers performed in concerts in the year 2023?", "| stadium : stadium_id , location , name , capacity , highest , lowest , average | singer : singer_id , name , country , song_name , song_release_year , age , is_male | concert : concert_id , concert_name , theme , stadium_id , year | singer_in_concert : concert_id , singer_id | concert.stadium_id = stadium.stadium_id | singer_in_concert.singer_id = singer.singer_id | singer_in_concert.concert_id = concert.concert_id |"],
            ["For students who have pets, how many pets does each student have? List their ids instead of names.", "| student : stuid , lname , fname , age , sex , major , advisor , city_code | has_pet : stuid , petid | pets : petid , pettype , pet_age , weight | has_pet.stuid = student.stuid | has_pet.petid = pets.petid |"],
            ["Show location and name for all stadiums with a capacity between 5000 and 10000.", "| stadium : stadium_id , location , name , capacity , highest , lowest , average | singer : singer_id , name , country , song_name , song_release_year , age , is_male | concert : concert_id , concert_name , theme , stadium_id , year | singer_in_concert : concert_id , singer_id | concert.stadium_id = stadium.stadium_id | singer_in_concert.singer_id = singer.singer_id | singer_in_concert.concert_id = concert.concert_id |"],
            ["What are the number of concerts that occurred in the stadium with the largest capacity ?", "| stadium : stadium_id , location , name , capacity , highest , lowest , average | singer : singer_id , name , country , song_name , song_release_year , age , is_male | concert : concert_id , concert_name , theme , stadium_id , year | singer_in_concert : concert_id , singer_id | concert.stadium_id = stadium.stadium_id | singer_in_concert.singer_id = singer.singer_id | singer_in_concert.concert_id = concert.concert_id |"],
            ["Which student has the oldest pet?", "| student : stuid , lname , fname , age , sex , major , advisor , city_code | has_pet : stuid , petid | pets : petid , pettype , pet_age , weight | has_pet.stuid = student.stuid | has_pet.petid = pets.petid |"],
            ["List the names of all singers who performed in a concert with the theme 'Rock'", "| stadium : stadium_id , location , name , capacity , highest , lowest , average | singer : singer_id , name , country , song_name , song_release_year , age , is_male | concert : concert_id , concert_name , theme , stadium_id , year | singer_in_concert : concert_id , singer_id | concert.stadium_id = stadium.stadium_id | singer_in_concert.singer_id = singer.singer_id | singer_in_concert.concert_id = concert.concert_id |"],
            ["List all students who don't have pets.", "| student : stuid , lname , fname , age , sex , major , advisor , city_code | has_pet : stuid , petid | pets : petid , pettype , pet_age , weight | has_pet.stuid = student.stuid | has_pet.petid = pets.petid |"],
        ], inputs=[input_text, db_info, temperature, top_p, top_k, repetition_penalty, format_sql, stop_sequence], fn=bot, cache_examples=False, outputs=output_box)


    readme_content = requests.get(f"https://huggingface.co/{merged_model}/raw/main/README.md").text
    readme_content = re.sub('---.*?---', '', readme_content, flags=re.DOTALL) #Remove YAML front matter

    with gr.Accordion("📖 Model Readme", open=True):
        readme = gr.Markdown(
            readme_content,
        )
    
    with gr.Accordion("More Options:", open=False):
        log = gr.Checkbox(label="Log to Firebase", value=True, interactive=True)
    
    # When the button is clicked, call the generate function, inputs are taken from the UI elements, outputs are sent to outputs elements
    run_button.click(fn=bot, inputs=[input_text, db_info, temperature, top_p, top_k, repetition_penalty, format_sql, stop_sequence, log], outputs=output_box, api_name="txt2sql")
    

demo.queue(concurrency_count=1, max_size=20).launch(debug=True)