added local demo

app-local.py

@@ -0,0 +1,325 @@
+import os
+import gradio as gr
+import sqlite3
+import sqlparse
+import requests
+import time
+import re
+import platform
+import openai
+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()}")
+
+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="cpu",
+    low_cpu_mem_usage=True
+    #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, query):
+    pre_prompt = "Generate a SQLite database with dummy data for this database, output the SQL code in a SQL code block. Make sure you add dummy data relevant to the question and query.\n\n"
+    prompt = pre_prompt + db_info + "\n\nQuestion: " + question + "\nQuery: " + query
+
+    while True:
+        try:
+            response = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {"role": "user", "content": prompt}
+                ],
+                #temperature=0.7,
+            )
+            response_text = response['choices'][0]['message']['content']
+            
+            db_code = extract_db_code(response_text)
+
+            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):
+    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)
+
+    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
+            db_code = generate_dummy_db(db_info, input_message, query)
+            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 the first response
+    output = responses[0] if responses 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("""
+        <h1 style="text-align: center">SQL Skeleton WizardCoder Demo</h1>
+        <h3 style="text-align: center">🕷️☠️🧙‍♂️ Generate SQL queries from Natural Language 🕷️☠️🧙‍♂️</h3>
+        <div style="max-width: 450px; margin: auto; text-align: center">
+            <p style="font-size: 12px; text-align: center">⚠️ 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.</p>
+        </div>
+    """)
+
+    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"""
+        <p>🌐 Leveraging the <a href='https://huggingface.co/{quantized_model}'><strong>bitsandbytes 8-bit version</strong></a> of <a href='https://huggingface.co/{merged_model}'><strong>{merged_model}</strong></a> model.</p>
+        <p>🔗 How it's made: <a href='https://huggingface.co/{initial_model}'><strong>{initial_model}</strong></a> was finetuned to create <a href='https://huggingface.co/{lora_model}'><strong>{lora_model}</strong></a>, then merged together to create <a href='https://huggingface.co/{merged_model}'><strong>{merged_model}</strong></a>.</p>
+        <p>📉 Fine-tuning was performed using QLoRA techniques on the <a href='https://huggingface.co/datasets/{dataset}'><strong>{dataset}</strong></a> dataset. You can view training metrics on the <a href='https://huggingface.co/{lora_model}'><strong>QLoRa adapter HF Repo</strong></a>.</p>
+        <p>📊 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.</a></p>
+    """)
+
+    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)