import os from threading import Event, Thread from transformers import ( AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer, ) import gradio as gr import torch import sqlparse model_name = os.getenv("HF_MODEL_NAME", None) tok = AutoTokenizer.from_pretrained(model_name) max_new_tokens = 1024 print(f"Starting to load the model {model_name}") m = AutoModelForCausalLM.from_pretrained( model_name, device_map=0, #load_in_8bit=True, ) m.config.pad_token_id = m.config.eos_token_id m.generation_config.pad_token_id = m.config.eos_token_id stop_tokens = [";", "###", "Result"] stop_token_ids = tok.convert_tokens_to_ids(stop_tokens) print(f"Successfully loaded the model {model_name} into memory") class StopOnTokens(StoppingCriteria): def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: for stop_id in stop_token_ids: if input_ids[0][-1] == stop_id: return True return False def bot(input_message: str, db_info="", temperature=0.1, top_p=0.9, top_k=0, repetition_penalty=1.08): stop = StopOnTokens() # Format the user's input message messages = f"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n\nConvert text to sql: {input_message} {db_info}\n\n### Response:\n\n" input_ids = tok(messages, return_tensors="pt").input_ids input_ids = input_ids.to(m.device) streamer = TextIteratorStreamer(tok, timeout=100.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( input_ids=input_ids, max_new_tokens=max_new_tokens, temperature=temperature, do_sample=temperature > 0.0, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty, streamer=streamer, stopping_criteria=StoppingCriteriaList([stop]), ) stream_complete = Event() def generate_and_signal_complete(): m.generate(**generate_kwargs) stream_complete.set() t1 = Thread(target=generate_and_signal_complete) t1.start() partial_text = "" for new_text in streamer: partial_text += new_text # Split the text by "|", and get the last element in the list which should be the final query try: final_query = partial_text.split("|")[1].strip() except Exception: final_query = partial_text try: # Attempt to format SQL query using sqlparse formatted_query = sqlparse.format(final_query, reindent=True, keyword_case='upper') except Exception: # If formatting fails, use the original, unformatted query formatted_query = final_query # Convert SQL to markdown (not required, but just to show how to use the markdown module) final_query_markdown = f"{formatted_query}" return final_query_markdown with gr.Blocks(theme='gradio/soft') as demo: header = gr.HTML("""

SQL Skeleton WizardCoder Demo

🧙‍♂️ Generate SQL queries from Natural Language 🧙‍♂️

""") output_box = gr.Code(label="Generated SQL", lines=2, interactive=True) 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') with gr.Accordion("Hyperparameters", open=False): temperature = gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.5, 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) run_button = gr.Button("Generate SQL", variant="primary") with gr.Accordion("Examples", open=True): examples = gr.Examples([ ["What is the average, minimum, and maximum age for all French singers?", "| 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 |"], ["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 |"], ["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 |"], ["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 |"] ], inputs=[input_text, db_info, temperature, top_p, top_k, repetition_penalty], fn=bot) bitsandbytes_model = "richardr1126/spider-skeleton-wizard-coder-8bit" merged_model = "richardr1126/spider-skeleton-wizard-coder-merged" initial_model = "WizardLM/WizardCoder-15B-V1.0" finetuned_model = "richardr1126/spider-skeleton-wizard-coder-qlora" dataset = "richardr1126/spider-skeleton-context-instruct" footer = gr.HTML(f"""

🛠️ If you want you can duplicate this Space, then change the HF_MODEL_REPO spaces env varaible to use any Transformers model.

🌐 Leveraging the bitsandbytes 8-bit version of {merged_model} model.

🔗 How it's made: {initial_model} was finetuned to create {finetuned_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.

""") run_button.click(fn=bot, inputs=[input_text, db_info, temperature, top_p, top_k, repetition_penalty], outputs=output_box, api_name="txt2sql") demo.queue(concurrency_count=1, max_size=10).launch()