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_flant5base_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.
    """
    prompt = f"""Context:
{context}

Query:
{query}

Response:
"""
    # Tokenize the prompt and move to device
    inputs = tokenizer(prompt, return_tensors="pt").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
    generated_ids = model.generate(
        input_ids=inputs["input_ids"],
        decoder_start_token_id=model.config.decoder_start_token_id,
        max_new_tokens=250,
        temperature=0.0,      # Deterministic output
        num_beams=3,          # Beam search for improved quality
        early_stopping=True,  # Stop when output is complete
    )
    
    # Decode and return the generated SQL statement
    return tokenizer.decode(generated_ids[0], skip_special_tokens=True)

# 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."
)

iface.launch()