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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
print("🚀 Loading model...")
# Load your merged model
model_name = "Abhisek987/llama-3.2-sql-merged"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype=torch.float16,
low_cpu_mem_usage=True
)
print("✅ Model loaded successfully!")
def generate_sql(database, question):
"""Generate SQL query from natural language question"""
prompt = f"""### Instruction:
You are a SQL expert. Generate a SQL query to answer the given question for the specified database.
### Input:
Database: {database}
Question: {question}
### Response:
"""
# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=256,
temperature=0.1,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id
)
# Decode and extract SQL
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
sql_query = result.split("### Response:")[-1].strip()
return sql_query
# Create Gradio interface
demo = gr.Interface(
fn=generate_sql,
inputs=[
gr.Textbox(
label="Database Name",
placeholder="e.g., employees, sales, customers",
value="employees"
),
gr.Textbox(
label="Question",
placeholder="e.g., Show all employees with salary above 60000",
lines=3
)
],
outputs=gr.Textbox(
label="Generated SQL Query",
lines=5
),
title="🤖 Text-to-SQL Generator",
description="Fine-tuned Llama 3.2 3B model for SQL query generation using LoRA. Enter a database name and your question in natural language.",
examples=[
["employees", "Show all employees with salary above 60000"],
["sales", "Show me the top 5 products by total sales"],
["customers", "How many customers are from each country?"],
["orders", "Find all orders placed in the last 30 days"]
]
)
# Launch without additional parameters for HF Spaces
demo.launch() |