⚠️ Should take 30-60s to generate
""") 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"""🌐 Leveraging the 4-bit GGML version of {merged_model} model.
🔗 How it's made: {initial_model} was finetuned to create {lora_model}, then merged together to create {merged_model}.
📉 Fine-tuning was performed using QLoRA techniques on the {dataset} dataset. You can view training metrics on the QLoRa adapter HF Repo.
📊 All inputs/outputs are logged to Firebase to see how the model is doing.
""") 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)