import os import gradio as gr import sqlparse import requests from time import sleep import re import platform # Additional Firebase imports import firebase_admin from firebase_admin import credentials, firestore import json import base64 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-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" def log_to_firestore(input_message, db_info, temperature, response_text): # For 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() 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) 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=0.1, top_p=0.9, top_k=0, repetition_penalty=1.08, format_sql=True, stop_sequence="###", log=False): # 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" stop_sequence = stop_sequence.split(",") stop = ["###"] + stop_sequence payload = { "prompt": messages, "temperature": temperature, "top_p": top_p, "top_k": top_k, "top_a": 0, "n": 1, "max_context_length": 2048, "max_length": 256, "rep_pen": repetition_penalty, "sampler_order": [6,0,1,3,4,2,5], "stop_sequence": stop, } headers = { "Content-Type": "application/json", "ngrok-skip-browser-warning": "1" # added this line } for _ in range(3): # Try 3 times 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] # Log the request to Firestore formatted_query = format(response_text) log_to_firestore(input_message, db_info, temperature, formatted_query if format_sql else response_text) if format_sql: return formatted_query else: return 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: # 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 🕷️☠️🧙‍♂️

""") output_box = gr.Code(label="Generated SQL", lines=2, interactive=True) note = gr.HTML("""

⚠️ 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') with gr.Accordion("Options", 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) format_sql = gr.Checkbox(label="Format SQL + Remove Skeleton", value=True, interactive=True) stop_sequence = gr.Textbox(lines=1, value="Explanation,Note", label='Extra Stop Sequence') # Generate button UI element run_button = gr.Button("Generate SQL", variant="primary") # 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, stop_sequence], outputs=output_box, api_name="txt2sql") ## 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 help me see where the model still needs improvements..

""") 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 |"], ["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' |"], ["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 |"], ], 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([ ["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=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, ) demo.queue(concurrency_count=1, max_size=20).launch(debug=True)