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ryanwang-trt commited on 27 days ago

Commit

7493570

1 Parent(s): 87e5f2c

Deploy SQLator backend

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Files changed (5) hide show

.dockerignore +29 -0
Dockerfile +27 -0
app.py +356 -0
config.py +22 -0
requirements.txt +9 -0

.dockerignore ADDED Viewed

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+.git
+.gitignore
+.gitattributes
+.github
+.claude
+.vscode
+.idea
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.pytest_cache
+.mypy_cache
+.ruff_cache
+.venv
+venv
+env
+ENV
+chrome-extension
+data
+models
+*.log
+demo.gif
+README.md
+Dockerfile
+.dockerignore

Dockerfile ADDED Viewed

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+FROM python:3.11-slim
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH \
+    HF_HOME=/home/user/.cache/huggingface \
+    PYTHONUNBUFFERED=1
+WORKDIR /home/user/app
+COPY --chown=user requirements.txt .
+RUN pip install --no-cache-dir --user \
+        --extra-index-url https://download.pytorch.org/whl/cpu \
+        -r requirements.txt \
+    && pip install --no-cache-dir --user gunicorn
+COPY --chown=user . .
+EXPOSE 7860
+CMD ["gunicorn", "-w", "1", "-t", "300", "-b", "0.0.0.0:7860", "app:app"]

app.py ADDED Viewed

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+import logging
+import os
+import torch
+from flask import Flask, request, render_template_string, jsonify
+from flask_cors import CORS
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from config import MODEL_PATH, HF_MODEL_ID, MAX_INPUT_LENGTH, MAX_OUTPUT_LENGTH, NUM_BEAMS, PROMPT_TEMPLATE, MAX_QUESTION_LENGTH, MAX_SCHEMA_LENGTH
+from schema import truncate_schema
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+log = logging.getLogger(__name__)
+app = Flask(__name__)
+CORS(app)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+tokenizer = None
+model = None
+def get_model():
+    global tokenizer, model
+    if model is None:
+        if os.path.exists(MODEL_PATH):
+            source = MODEL_PATH
+        else:
+            log.info(f"Local model not found at '{MODEL_PATH}', downloading from HuggingFace: {HF_MODEL_ID}")
+            source = HF_MODEL_ID
+        tokenizer = AutoTokenizer.from_pretrained(source)
+        model = AutoModelForSeq2SeqLM.from_pretrained(source)
+        model = model.to(device)
+        model.eval()
+        log.info(f"Model loaded from {source} on {device}")
+    return tokenizer, model
+def predict(question, db_id="unknown", schema="unknown"):
+    schema = truncate_schema(schema, MAX_SCHEMA_LENGTH)
+    input_text = PROMPT_TEMPLATE.format(db_id=db_id, schema=schema, question=question)
+    tokenizer, model = get_model()
+    tokenized_input = tokenizer(input_text, max_length=MAX_INPUT_LENGTH, truncation=True, return_tensors="pt")
+    tokenized_outputs = model.generate(
+        input_ids=tokenized_input["input_ids"].to(device),
+        attention_mask=tokenized_input["attention_mask"].to(device),
+        max_length=MAX_OUTPUT_LENGTH,
+        num_beams=NUM_BEAMS,
+    )
+    return tokenizer.decode(tokenized_outputs[0], skip_special_tokens=True)
+HTML = """
+<!DOCTYPE html>
+<html>
+<head>
+    <title>SQLator — Natural Language to SQL</title>
+    <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;700&family=DM+Sans:wght@400;500;700&display=swap" rel="stylesheet">
+    <style>
+        * { margin: 0; padding: 0; box-sizing: border-box; }
+        body {
+            font-family: 'DM Sans', sans-serif;
+            min-height: 100vh;
+            background: #0a0a0f;
+            color: #e0e0e0;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            overflow: hidden;
+        }
+        /* animated background grid */
+        body::before {
+            content: '';
+            position: fixed;
+            top: 0; left: 0; right: 0; bottom: 0;
+            background-image:
+                linear-gradient(rgba(56, 189, 248, 0.03) 1px, transparent 1px),
+                linear-gradient(90deg, rgba(56, 189, 248, 0.03) 1px, transparent 1px);
+            background-size: 60px 60px;
+            z-index: 0;
+        }
+        /* glow orb */
+        body::after {
+            content: '';
+            position: fixed;
+            top: -200px; right: -200px;
+            width: 600px; height: 600px;
+            background: radial-gradient(circle, rgba(56, 189, 248, 0.08), transparent 70%);
+            border-radius: 50%;
+            z-index: 0;
+        }
+        .container {
+            position: relative;
+            z-index: 1;
+            width: 100%;
+            max-width: 680px;
+            padding: 20px;
+        }
+        .badge {
+            display: inline-block;
+            padding: 6px 14px;
+            background: rgba(56, 189, 248, 0.1);
+            border: 1px solid rgba(56, 189, 248, 0.2);
+            border-radius: 100px;
+            font-size: 12px;
+            font-weight: 500;
+            color: #38bdf8;
+            letter-spacing: 1.5px;
+            text-transform: uppercase;
+            margin-bottom: 20px;
+        }
+        h1 {
+            font-family: 'JetBrains Mono', monospace;
+            font-size: 42px;
+            font-weight: 700;
+            color: #ffffff;
+            line-height: 1.1;
+            margin-bottom: 8px;
+        }
+        h1 span {
+            background: linear-gradient(135deg, #38bdf8, #818cf8);
+            -webkit-background-clip: text;
+            -webkit-text-fill-color: transparent;
+        }
+        .subtitle {
+            color: #6b7280;
+            font-size: 15px;
+            margin-bottom: 40px;
+        }
+        .card {
+            background: rgba(255, 255, 255, 0.03);
+            border: 1px solid rgba(255, 255, 255, 0.06);
+            border-radius: 16px;
+            padding: 32px;
+            backdrop-filter: blur(20px);
+        }
+        label {
+            display: block;
+            font-size: 13px;
+            font-weight: 500;
+            color: #9ca3af;
+            margin-bottom: 8px;
+            letter-spacing: 0.5px;
+        }
+        input[type=text] {
+            width: 100%;
+            padding: 14px 16px;
+            background: rgba(0, 0, 0, 0.4);
+            border: 1px solid rgba(255, 255, 255, 0.08);
+            border-radius: 10px;
+            color: #f0f0f0;
+            font-family: 'DM Sans', sans-serif;
+            font-size: 15px;
+            outline: none;
+            transition: border-color 0.2s;
+            margin-bottom: 20px;
+        }
+        input[type=text]:focus, textarea:focus {
+            border-color: rgba(56, 189, 248, 0.4);
+        }
+        input[type=text]::placeholder, textarea::placeholder {
+            color: #4b5563;
+        }
+        textarea {
+            width: 100%;
+            padding: 14px 16px;
+            background: rgba(0, 0, 0, 0.4);
+            border: 1px solid rgba(255, 255, 255, 0.08);
+            border-radius: 10px;
+            color: #f0f0f0;
+            font-family: 'JetBrains Mono', monospace;
+            font-size: 13px;
+            outline: none;
+            transition: border-color 0.2s;
+            margin-bottom: 20px;
+            resize: vertical;
+        }
+        button {
+            width: 100%;
+            padding: 14px;
+            background: linear-gradient(135deg, #38bdf8, #818cf8);
+            color: #fff;
+            font-family: 'DM Sans', sans-serif;
+            font-size: 15px;
+            font-weight: 600;
+            border: none;
+            border-radius: 10px;
+            cursor: pointer;
+            transition: opacity 0.2s, transform 0.1s;
+            letter-spacing: 0.3px;
+        }
+        button:hover { opacity: 0.9; }
+        button:active { transform: scale(0.98); }
+        .result {
+            margin-top: 28px;
+            padding-top: 28px;
+            border-top: 1px solid rgba(255, 255, 255, 0.06);
+        }
+        .result-label {
+            font-size: 12px;
+            font-weight: 500;
+            color: #6b7280;
+            letter-spacing: 1px;
+            text-transform: uppercase;
+            margin-bottom: 6px;
+        }
+        .result-question {
+            color: #d1d5db;
+            font-size: 15px;
+            margin-bottom: 16px;
+        }
+        .sql-output {
+            background: rgba(0, 0, 0, 0.5);
+            border: 1px solid rgba(56, 189, 248, 0.15);
+            border-radius: 10px;
+            padding: 16px 20px;
+            font-family: 'JetBrains Mono', monospace;
+            font-size: 14px;
+            color: #38bdf8;
+            line-height: 1.6;
+            overflow-x: auto;
+        }
+        .footer {
+            text-align: center;
+            margin-top: 32px;
+            font-size: 12px;
+            color: #374151;
+        }
+        .footer a {
+            color: #4b5563;
+            text-decoration: none;
+        }
+        /* fade in animation */
+        .container { animation: fadeUp 0.6s ease-out; }
+        @keyframes fadeUp {
+            from { opacity: 0; transform: translateY(20px); }
+            to { opacity: 1; transform: translateY(0); }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="badge">Fine-tuned CodeT5+ Model</div>
+        <h1>SQL<span>ator</span></h1>
+        <p class="subtitle">Ask a question in plain English. Get a SQL query back.</p>
+        <div class="card">
+            <form method="POST">
+                <label>YOUR QUESTION</label>
+                <input type="text" name="question" placeholder="e.g. how many employees are in each department" value="{{ question or '' }}" autofocus>
+                <label>DATABASE (OPTIONAL)</label>
+                <input type="text" name="db_id" placeholder="e.g. concert_singer" value="{{ db_id or '' }}">
+                <label>SCHEMA (OPTIONAL)</label>
+                <textarea name="schema" rows="3" placeholder="e.g. singer(singer_id, name, country, age), concert(concert_id, concert_name, theme)">{{ schema or '' }}</textarea>
+                <button type="submit">Generate SQL →</button>
+            </form>
+            {% if error %}
+            <div class="result">
+                <div style="color: #f87171; font-size: 14px;">{{ error }}</div>
+            </div>
+            {% endif %}
+            {% if sql %}
+            <div class="result">
+                <div class="result-label">Input</div>
+                <div class="result-question">{{ question }}</div>
+                <div class="result-label">Generated SQL</div>
+                <div class="sql-output">{{ sql }}</div>
+            </div>
+            {% endif %}
+        </div>
+        <div class="footer">
+            Built with CodeT5+ 220M + PyTorch — <a href="https://github.com">View on GitHub</a>
+        </div>
+    </div>
+</body>
+</html>
+"""
+@app.route("/health", methods=["GET"])
+def health():
+    return jsonify({"status": "ok"})
+@app.route("/predict", methods=["POST"])
+def predict_api():
+    data = request.get_json(silent=True) or {}
+    question = (data.get("question") or "").strip()
+    db_id = (data.get("db_id") or "").strip() or "unknown"
+    if not question:
+        return jsonify({"error": "Please enter a question."}), 400
+    if len(question) > MAX_QUESTION_LENGTH:
+        return jsonify({"error": f"Question is too long (max {MAX_QUESTION_LENGTH} characters)."}), 400
+    try:
+        log.info(f"API predict: question='{question}' db_id='{db_id}'")
+        sql = predict(question, db_id, schema="unknown")
+        return jsonify({"sql": sql})
+    except Exception as e:
+        log.exception("Prediction failed")
+        return jsonify({"error": f"Inference failed: {e}"}), 500
+@app.route("/", methods=["GET", "POST"])
+def home():
+    question = None
+    db_id = None
+    schema = None
+    sql = None
+    error = None
+    if request.method == "POST":
+        question = request.form.get("question", "").strip()
+        db_id = request.form.get("db_id", "").strip() or "unknown"
+        schema = request.form.get("schema", "").strip() or "unknown"
+        if not question:
+            error = "Please enter a question."
+        elif len(question) > MAX_QUESTION_LENGTH:
+            error = f"Question is too long (max {MAX_QUESTION_LENGTH} characters)."
+        else:
+            log.info(f"Predicting for question='{question}' db_id='{db_id}'")
+            sql = predict(question, db_id, schema=schema)
+    return render_template_string(HTML, question=question, db_id=db_id, schema=schema, sql=sql, error=error)
+if __name__ == "__main__":
+    debug = os.getenv("FLASK_DEBUG", "false").lower() == "true"
+    app.run(debug=debug)

config.py ADDED Viewed

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+import os
+MODEL_PATH = os.getenv("MODEL_PATH", "models/t5-sql")
+BASE_MODEL = os.getenv("BASE_MODEL", "Salesforce/codet5p-220m")
+MAX_INPUT_LENGTH = 512
+MAX_OUTPUT_LENGTH = 128
+BATCH_SIZE = 2
+ACCUMULATION_STEPS = 4
+NUM_EPOCHS = 6
+LEARNING_RATE = 1e-4
+WARMUP_RATIO = 0.1
+NUM_BEAMS = 5
+MAX_SCHEMA_LENGTH = 400
+HF_MODEL_ID = os.getenv("HF_MODEL_ID", "ryanwang-trt/t5-sql")
+PROMPT_TEMPLATE = "translate English to SQL [database: {db_id} | tables: {schema}]: {question}"
+SPIDER_DB_DIR = os.getenv("SPIDER_DB_DIR", "data/database")
+MAX_QUESTION_LENGTH = 500

requirements.txt ADDED Viewed

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+torch
+transformers
+datasets
+scikit-learn
+accelerate
+flask
+flask-cors
+huggingface_hub
+python-dotenv