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import gradio as gr
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Set up device (GPU if available)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load the fine-tuned model and tokenizer
model_name = "aarohanverma/text2sql-flan-t5-base-qlora-finetuned" # Replace with your model repository name
model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device)
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
def generate_sql(context: str, query: str) -> str:
"""
Generates a SQL query given the provided context and natural language query.
Constructs a prompt from the inputs, then performs deterministic generation
with beam search and repetition handling.
"""
prompt = f"""Context:
{context}
Query:
{query}
Response:
"""
# Tokenize the prompt and move to device
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512).to(device)
# Ensure decoder_start_token_id is set for encoder-decoder generation
if model.config.decoder_start_token_id is None:
model.config.decoder_start_token_id = tokenizer.pad_token_id
# Generate the SQL output with optimized parameters
generated_ids = model.generate(
input_ids=inputs["input_ids"],
decoder_start_token_id=model.config.decoder_start_token_id,
max_new_tokens=100,
temperature=0.1,
num_beams=5,
repetition_penalty=1.2,
early_stopping=True,
)
# Decode and clean the generated SQL statement
generated_sql = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
generated_sql = generated_sql.split(";")[0] + ";" # ✅ Ensures only the first valid SQL query is returned
return generated_sql
# Create Gradio interface with two input boxes: one for context and one for query
iface = gr.Interface(
fn=generate_sql,
inputs=[
gr.Textbox(lines=8, label="Context", placeholder="Enter table schema, sample data, etc."),
gr.Textbox(lines=2, label="Query", placeholder="Enter your natural language query here...")
],
outputs="text",
title="Text-to-SQL Generator",
description="Enter your own context (e.g., database schema and sample data) and a natural language query. The model will generate the corresponding SQL statement.",
theme="compact",
allow_flagging="never"
)
iface.launch()
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