# app.py - Optimized for Hugging Face Spaces (Unsloth = 2-4x faster)

import torch
import gradio as gr
from unsloth import FastLanguageModel

# ────────────────────────────────────────────────────────────────
BASE_MODEL = "unsloth/Phi-3-mini-4k-instruct-bnb-4bit"
LORA_PATH  = "saadkhi/SQL_Chat_finetuned_model"

MAX_NEW_TOKENS = 180
TEMPERATURE    = 0.0   # Greedy = fastest & deterministic
# ────────────────────────────────────────────────────────────────

print("Loading base model with Unsloth (4-bit)...")
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = BASE_MODEL,
    max_seq_length = 2048,
    dtype = None,               # Auto: bfloat16 on GPU
    load_in_4bit = True,        # Already quantized base
)

print("Applying your LoRA adapter...")
model = FastLanguageModel.get_peft_model(
    model,
    r = 64,                     # Match your original rank
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj"],
    lora_alpha = 128,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
)

# Enable 2x faster inference kernels
FastLanguageModel.for_inference(model)

print("Model ready! (very fast now)")

# ────────────────────────────────────────────────────────────────
def generate_sql(prompt: str):
    messages = [{"role": "user", "content": prompt}]
    
    inputs = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to("cuda" if torch.cuda.is_available() else "cpu")

    outputs = model.generate(
        input_ids = inputs,
        max_new_tokens = MAX_NEW_TOKENS,
        temperature = TEMPERATURE,
        do_sample = (TEMPERATURE > 0.01),
        use_cache = True,
        pad_token_id = tokenizer.eos_token_id,
    )

    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Extract only assistant response
    if "<|assistant|>" in response:
        response = response.split("<|assistant|>", 1)[-1].strip()
    response = response.split("<|end|>")[0].strip()

    return response

# ────────────────────────────────────────────────────────────────
demo = gr.Interface(
    fn = generate_sql,
    inputs = gr.Textbox(
        label = "Ask SQL question",
        placeholder = "Delete duplicate rows from users table based on email",
        lines = 3
    ),
    outputs = gr.Textbox(label="Generated SQL"),
    title = "SQL Chatbot - Ultra Fast (Unsloth)",
    description = "Phi-3-mini 4-bit + your LoRA",
    examples = [
        ["Find duplicate emails in users table"],
        ["Top 5 highest paid employees"],
        ["Count orders per customer last month"]
    ]
)

if __name__ == "__main__":
    demo.launch()