import os import gradio as gr import sqlite3 import sqlparse import requests import time import re import platform import openai import random from transformers import ( AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, ) # Additional Firebase imports import firebase_admin from firebase_admin import credentials, firestore import json import base64 import torch print(f"Running on {platform.system()}") if platform.system() == "Windows" or platform.system() == "Darwin": from dotenv import load_dotenv load_dotenv() quantized_model = "richardr1126/spider-skeleton-wizard-coder-8bit" 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" 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 print(f"Successfully loaded the model {model_name} into memory") ################# Firebase code ################# # Initialize Firebase base64_string = os.getenv('FIREBASE') base64_bytes = base64_string.encode('utf-8') json_bytes = base64.b64decode(base64_bytes) json_data = json_bytes.decode('utf-8') firebase_auth = json.loads(json_data) # Load credentials and initialize Firestore cred = credentials.Certificate(firebase_auth) firebase_admin.initialize_app(cred) db = firestore.client() def log_message_to_firestore(input_message, db_info, temperature, response_text): doc_ref = db.collection('logs').document() log_data = { 'timestamp': firestore.SERVER_TIMESTAMP, 'temperature': temperature, 'db_info': db_info, 'input': input_message, 'output': response_text, } doc_ref.set(log_data) rated_outputs = set() # set to store already rated outputs def log_rating_to_firestore(input_message, db_info, temperature, response_text, rating): global rated_outputs output_id = f"{input_message} {db_info} {response_text} {temperature}" if output_id in rated_outputs: gr.Warning("You've already rated this output!") return if not input_message or not response_text or not rating: gr.Info("You haven't asked a question yet!") return rated_outputs.add(output_id) doc_ref = db.collection('ratings').document() log_data = { 'timestamp': firestore.SERVER_TIMESTAMP, 'temperature': temperature, 'db_info': db_info, 'input': input_message, 'output': response_text, 'rating': rating, } doc_ref.set(log_data) gr.Info("Thanks for your feedback!") ############### End Firebase code ############### 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 extract_db_code(text): pattern = r'```(?:\w+)?\s?(.*?)```' matches = re.findall(pattern, text, re.DOTALL) return [match.strip() for match in matches] def generate_dummy_db(db_info, question): pre_prompt = "Generate a SQLite database with dummy data for this database. Make sure you add dummy data relevant to the question and don't write any SELECT statements or actual queries.\n\n" prompt = pre_prompt + db_info + "\n\nQuestion: " + question while True: try: response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[ {"role": "system", "content": "You are a SQLite dummy database generator. 1. You will create the specified dummy database. 2. Insert the dummy data. and 3. Output the only the code in a SQL code block."}, {"role": "user", "content": prompt} ], #temperature=0.7, ) response_text = response['choices'][0]['message']['content'] db_code = extract_db_code(response_text) print(db_code) return db_code except Exception as e: print(f'Error occurred: {str(e)}') print('Waiting for 20 seconds before retrying...') time.sleep(20) def test_query_on_dummy_db(db_code, query): try: # Connect to an SQLite database in memory conn = sqlite3.connect(':memory:') cursor = conn.cursor() # Iterate over each extracted SQL block and split them into individual commands for sql_block in db_code: statements = sqlparse.split(sql_block) # Execute each SQL command for statement in statements: if statement: cursor.execute(statement) # Run the provided test query against the database cursor.execute(query) print(cursor.fetchall()) # Close the connection conn.close() # If everything executed without errors, return True return True except Exception as e: print(f"Error encountered: {e}") return False def generate(input_message: str, db_info="", temperature=0.2, top_p=0.9, top_k=0, repetition_penalty=1.08, format_sql=True, log=False, num_return_sequences=1, num_beams=1, do_sample=False): if num_return_sequences > num_beams: gr.Warning("Num return sequences must be less than or equal to num beams.") stop_token_ids = tok.convert_tokens_to_ids(["###"]) 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 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) generate_kwargs = dict( input_ids=input_ids, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty, #streamer=streamer, stopping_criteria=StoppingCriteriaList([stop]), num_return_sequences=num_return_sequences, num_beams=num_beams, do_sample=do_sample, ) tokens = m.generate(**generate_kwargs) db_code = None if (num_return_sequences > 1): db_code = generate_dummy_db(db_info, input_message) responses = [] for response in tokens: response_text = tok.decode(response, skip_special_tokens=True) # Only take what comes after ### Response: response_text = response_text.split("### Response:")[1].strip() query = format(response_text) if format_sql else response_text if (num_return_sequences > 1): query = query.replace("\n", " ").replace("\t", " ").strip() # Test against dummy database success = test_query_on_dummy_db(db_code, query) # Format again query = format(query) if format_sql else query if success: responses.append(query) else: responses.append(query) # Choose a random response from responses output = responses[0] if len(responses) > 0 else "###" if log: # Log the request to Firestore log_message_to_firestore(input_message, db_info, temperature, output) return output # 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("""

SQL Skeleton WizardCoder Demo

🕷️☠️🧙‍♂️ Generate SQL queries from Natural Language 🕷️☠️🧙‍♂️

⚠️ Should take 30-60s to generate. Please rate the response, it helps a lot. If you get a blank output, the model server is currently down, please try again another time.

""") output_box = gr.Code(label="Generated SQL", lines=2, interactive=False) with gr.Row(): rate_up = gr.Button("👍", variant="secondary") rate_down = gr.Button("👎", variant="secondary") input_text = gr.Textbox(lines=3, placeholder='Write your question here...', label='NL Input') db_info = gr.Textbox(lines=4, placeholder='Make sure to place your tables information inside || for better results. 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) with gr.Row(): run_button = gr.Button("Generate SQL", variant="primary") clear_button = gr.ClearButton(variant="secondary") 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) with gr.Accordion("Generation strategies", open=False): md_description = gr.Markdown("""Increasing num return sequences will increase the number of SQLs generated, but will still yield only the best output of the number of return sequences. SQLs are tested against the db info you provide.""") num_return_sequences = gr.Slider(label="Number of return sequences (to generate and test)", minimum=1, maximum=5, value=1, step=1) num_beams = gr.Slider(label="Num Beams", minimum=1, maximum=5, value=1, step=1) do_sample = gr.Checkbox(label="Do Sample", value=False, interactive=True) info = gr.HTML(f"""

🌐 Leveraging the bitsandbytes 8-bit 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. You can also leave a rating for each generated SQL the model produces, which gets sent to the database as well.

""") 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], 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], fn=generate, 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("Disabled Options:", open=False): log = gr.Checkbox(label="Log to Firebase", value=True, interactive=False) # 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=generate, inputs=[input_text, db_info, temperature, top_p, top_k, repetition_penalty, format_sql, log, num_return_sequences, num_beams, do_sample], outputs=output_box, api_name="txt2sql") clear_button.add([input_text, db_info, output_box]) # Firebase code - for rating the generated SQL (remove if you don't want to use Firebase) rate_up.click(fn=log_rating_to_firestore, inputs=[input_text, db_info, temperature, output_box, rate_up]) rate_down.click(fn=log_rating_to_firestore, inputs=[input_text, db_info, temperature, output_box, rate_down]) demo.queue(concurrency_count=1, max_size=20).launch(debug=True)