import os
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = os.getenv("MODEL_ID", "mistralai/Mistral-7B-Instruct-v0.3")
HF_TOKEN = os.getenv("HF_TOKEN", None)

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    token=HF_TOKEN,
    torch_dtype="auto",
    device_map="auto",
)

def draft_reply(subject, thread):
    system = (
        "You are an email assistant. Draft a reply email.\n"
        "- Be clear and polite.\n"
        "- Ask up to 2 clarifying questions if needed.\n"
        "- Do not invent facts.\n"
        "- Output ONLY the email body.\n"
    )
    user = f"Subject: {subject}\n\nEmail thread:\n{thread}\n\nWrite the reply now."

    messages = [{"role": "system", "content": system}, {"role": "user", "content": user}]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=250, temperature=0.4, do_sample=True)

    return tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip()

demo = gr.Interface(
    fn=draft_reply,
    inputs=[gr.Textbox(label="Subject"), gr.Textbox(label="Email Thread", lines=10)],
    outputs=gr.Textbox(label="Draft Reply", lines=12),
    title="Email Reply Drafting Assistant",
)

demo.launch()