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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
MODELS = {
"BM1_CS1_Syn (33M)": "withmartian/sql_interp_bm1_cs1_experiment_1.10",
"BM1_CS2_Syn (33M)": "withmartian/sql_interp_bm1_cs2_experiment_2.10",
"BM1_CS3_Syn (33M)": "withmartian/sql_interp_bm1_cs3_experiment_3.10",
"BM1_CS4_Syn (33M)": "withmartian/sql_interp_bm1_cs4_dataset_synonyms_experiment_1.1",
"BM1_CS5_Syn (33M)": "withmartian/sql_interp_bm1_cs5_dataset_synonyms_experiment_1.2",
"BM2_CS1_Syn (0.5B)": "withmartian/sql_interp_bm2_cs1_experiment_4.3",
"BM2_CS2_Syn (0.5B)": "withmartian/sql_interp_bm2_cs2_experiment_5.3",
"BM2_CS3_Syn (0.5B)": "withmartian/sql_interp_bm2_cs3_experiment_6.3",
"BM3_CS1_Syn (1B)": "withmartian/sql_interp_bm3_cs1_experiment_7.3",
"BM3_CS2_Syn (1B)": "withmartian/sql_interp_bm3_cs2_experiment_8.3",
"BM3_CS3_Syn (1B)": "withmartian/sql_interp_bm3_cs3_experiment_9.3",
}
model_cache = {}
def load_model(model_name):
if model_name not in model_cache:
model_id = MODELS[model_name]
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto"
)
model_cache[model_name] = (tokenizer, model)
return model_cache[model_name]
def generate_sql(model_name, instruction, schema, max_length=256, temperature=0.0):
if not model_name or not instruction or not schema:
return "Please fill in all fields and select a model"
try:
tokenizer, model = load_model(model_name)
prompt = f"### Instruction: {instruction} ### Context: {schema} ### Response:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_length=max_length,
temperature=temperature if temperature > 0 else 1.0,
do_sample=temperature > 0,
pad_token_id=tokenizer.eos_token_id
)
generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
if "### Response:" in generated:
sql = generated.split("### Response:")[-1].strip()
else:
sql = generated.strip()
return sql
except Exception as e:
return f"Error: {str(e)}"
def model_demo(shared_instruction, shared_schema):
gr.HTML("""
<div style="text-align: center; padding: 2rem; background: linear-gradient(135deg, #3A3A3A 0%, #4A4A4A 100%); border-radius: 16px; margin-bottom: 2rem;">
<h2 style="font-size: 2rem; font-weight: 700; margin-bottom: 0.5rem; color: white;">Interactive SQL Generation</h2>
<p style="font-size: 1rem; color: #D0D0D0; margin: 0;">Transform natural language into SQL</p>
</div>
""")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Configuration")
model_dropdown = gr.Dropdown(
choices=list(MODELS.keys()),
value="BM2_CS2_Syn (0.5B)",
label="Model Selection"
)
gr.HTML("""
<div style="background: #2A2A2A; border-radius: 12px; padding: 1.5rem; margin: 1.5rem 0;">
<h4 style="color: #FF6B4A; font-size: 1rem; margin: 0 0 1.5rem 0; font-weight: 700;">Model Guide</h4>
<div style="margin-bottom: 1rem;">
<div style="font-weight: 600; font-size: 0.9rem; margin-bottom: 0.4rem; color: #FFFFFF;">BM1 (33M parameters)</div>
<div style="font-size: 0.85rem; margin-left: 1rem; color: #FFFFFF;">TinyStories 33M fine-tuned</div>
</div>
<div style="margin-bottom: 1rem;">
<div style="font-weight: 600; font-size: 0.9rem; margin-bottom: 0.4rem; color: #FFFFFF;">BM2 (0.5B parameters)</div>
<div style="font-size: 0.85rem; margin-left: 1rem; color: #FFFFFF;">Qwen 2.5 0.5B fine-tuned</div>
</div>
<div style="margin-bottom: 1.5rem;">
<div style="font-weight: 600; font-size: 0.9rem; margin-bottom: 0.4rem; color: #FFFFFF;">BM3 (1B parameters)</div>
<div style="font-size: 0.85rem; margin-left: 1rem; color: #FFFFFF;">Llama 3.2 1B fine-tuned</div>
</div>
<div style="padding-top: 1rem; border-top: 1px solid #3A3A3A;">
<div style="color: #FF6B4A; font-weight: 600; font-size: 0.9rem; margin-bottom: 0.75rem;">Dataset Complexity</div>
<div style="font-size: 0.9rem; line-height: 2;">
<div style="color: #FFFFFF;"><strong style="color: #FFFFFF;">CS1:</strong> <span style="color: #FFFFFF;">Basic SELECT-FROM</span></div>
<div style="color: #FFFFFF;"><strong style="color: #FFFFFF;">CS2:</strong> <span style="color: #FFFFFF;">Adds ORDER BY</span></div>
<div style="color: #FFFFFF;"><strong style="color: #FFFFFF;">CS3:</strong> <span style="color: #FFFFFF;">Aggregations</span></div>
<div style="color: #FFFFFF;"><strong style="color: #FFFFFF;">CS4:</strong> <span style="color: #FFFFFF;">WHERE filters</span></div>
<div style="color: #FFFFFF;"><strong style="color: #FFFFFF;">CS5:</strong> <span style="color: #FFFFFF;">Multi-table JOINs</span></div>
</div>
</div>
</div>
""")
with gr.Column(scale=2):
gr.Markdown("### Your Query")
instruction = gr.Textbox(
label="Natural Language Query",
placeholder="e.g., Find all employees earning more than 50000 sorted by name",
lines=2,
value=""
)
schema = gr.Code(
label="Database Schema (SQL)",
language="sql",
value="""CREATE TABLE employees (
name VARCHAR(100),
salary INT,
department VARCHAR(100)
);""",
lines=6
)
with gr.Row():
max_length = gr.Slider(64, 512, value=256, step=32, label="Max Length")
temperature = gr.Slider(0.0, 1.0, value=0.0, step=0.1, label="Temperature")
generate_btn = gr.Button("Generate SQL", variant="primary", size="lg")
output = gr.Code(
label="Generated SQL Query",
language="sql",
lines=8
)
shared_instruction.change(
fn=lambda x: x,
inputs=shared_instruction,
outputs=instruction
)
shared_schema.change(
fn=lambda x: x,
inputs=shared_schema,
outputs=schema
)
generate_btn.click(
fn=generate_sql,
inputs=[model_dropdown, instruction, schema, max_length, temperature],
outputs=output
)
return {'instruction': instruction, 'schema': schema, 'output': output} |