import os import gradio as gr import sqlparse import requests from time import sleep import re def format(text): # Split the text by "|", and get the last element in the list which should be the final query try: final_query = text.split("|")[1].strip() except Exception: final_query = 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 def generate(input_message: str, db_info="", temperature=0.3, top_p=0.9, top_k=0, repetition_penalty=1.08): # 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" url = "https://e9f4be879d38-8269039109365193683.ngrok-free.app/api/v1/generate" payload = { "prompt": messages, "temperature": temperature, "top_p": top_p, "top_k": top_k, "top_a": 0, "n": 1, "max_context_length": 2048, "max_length": 512, "rep_pen": repetition_penalty, "sampler_order": [6,0,1,3,4,2,5], "stop_sequence": ["###", "Result"], } headers = { "Content-Type": "application/json", "ngrok-skip-browser-warning": "1" # added this line } for _ in range(3): try: response = requests.post(url, json=payload, headers=headers) response_text = response.json()["results"][0]["text"] response_text = response_text.replace("\n", "").replace("\t", " ") if response_text and response_text[-1] == ".": response_text = response_text[:-1] return format(response_text) except Exception as e: print(f'Error occurred: {str(e)}') print('Waiting for 10 seconds before retrying...') sleep(10) # Gradio UI Code 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=0.3, 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") run_button.click(fn=generate, inputs=[input_text, db_info, temperature, top_p, top_k, repetition_penalty], outputs=output_box, api_name="txt2sql") with gr.Accordion("Examples", open=True): 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 |"], ["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=generate, cache_examples=True, outputs=output_box) quantized_model = "richardr1126/spider-skeleton-wizard-coder-ggml" merged_model = "richardr1126/spider-skeleton-wizard-coder-merged" initial_model = "WizardLM/WizardCoder-15B-V1.0" lora_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 GGML model.

🌐 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.

""") 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, ) demo.queue(concurrency_count=1, max_size=10).launch(debug=True)