app.py

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

MODEL_ID = os.getenv("MODEL_ID", "JDhruv14/fullnfinal")

# --- System prompt (Gita persona) ---
GITA_SYSTEM_PROMPT = """You are Krishna — a compassionate, serene, and practical guider inspired by the Bhagavad Gita"""

# Load once (CPU until first call; device_map will move to GPU on first run)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else "auto",
    trust_remote_code=True,
)

# Ensure pad token exists (many chat models reuse EOS as PAD)
if tokenizer.pad_token_id is None and tokenizer.eos_token_id is not None:
    tokenizer.pad_token = tokenizer.eos_token

def _msgs_from_history(history, system_text):
    msgs = []
    if system_text:
        msgs.append({"role": "system", "content": system_text})
    if not history:
        return msgs

    # Support both new "messages" format and legacy (user, assistant) tuples
    if isinstance(history[0], dict) and "role" in history[0] and "content" in history[0]:
        for m in history:
            role, content = m.get("role"), m.get("content")
            if role in ("user", "assistant", "system") and content:
                msgs.append({"role": role, "content": content})
    else:
        for user, assistant in history:
            if user:
                msgs.append({"role": "user", "content": user})
            if assistant:
                msgs.append({"role": "assistant", "content": assistant})
    return msgs

def _eos_ids(tok):
    # Support ints/lists and optional <|im_end|>
    ids = set()
    if tok.eos_token_id is not None:
        if isinstance(tok.eos_token_id, (list, tuple)):
            ids.update(tok.eos_token_id)
        else:
            ids.add(tok.eos_token_id)
    try:
        im_end = tok.convert_tokens_to_ids("<|im_end|>")
        if im_end is not None and im_end != tok.unk_token_id:
            ids.add(im_end)
    except Exception:
        pass
    return list(ids)

def chat_fn(message, history, system_text, temperature, top_p, max_new, min_new):
    msgs = _msgs_from_history(history, system_text) + [{"role": "user", "content": message}]
    prompt = tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer([prompt], return_tensors="pt").to(model.device)

    eos = _eos_ids(tokenizer)
    gen_cfg_kwargs = dict(
        do_sample=True,
        temperature=float(temperature),
        top_p=float(top_p),
        max_new_tokens=int(max_new),
        min_new_tokens=int(min_new),
        repetition_penalty=1.02,
        no_repeat_ngram_size=3,
        pad_token_id=tokenizer.pad_token_id if tokenizer.pad_token_id is not None else tokenizer.eos_token_id,
    )
    if eos:
        gen_cfg_kwargs["eos_token_id"] = eos

    gen_cfg = GenerationConfig(**gen_cfg_kwargs)

    with torch.no_grad():
        out = model.generate(**inputs, generation_config=gen_cfg)

    new_tokens = out[:, inputs["input_ids"].shape[1]:]
    reply = tokenizer.batch_decode(new_tokens, skip_special_tokens=True)[0].strip()
    return reply

@spaces.GPU()
def gradio_fn(message, history):
    # Inject the Gita system prompt here
    return chat_fn(
        message=message,
        history=history,
        system_text=GITA_SYSTEM_PROMPT,
        temperature=0.7,
        top_p=0.95,
        max_new=512,
        min_new=0,
    )

with gr.Blocks(css="""
    :root { --chat-w: 520px; }

    html, body {
        height: 100%;
        overflow-y: hidden;               /* no page scroll */
        margin: 0;
    }
    /* Full-screen background image with a soft dark overlay */
    body {
        background:
          linear-gradient(0deg, rgba(0,0,0,.28), rgba(0,0,0,.28)),
          url("/file=bg.jpg") center / cover no-repeat fixed;  /* <- change filename if needed */
    }

    /* Left-aligned, narrower chat panel */
    .gradio-container {
        max-width: var(--chat-w);
        width: var(--chat-w);
        margin-left: 16px;
        margin-right: auto;
        padding: 20px;
        font-family: sans-serif;
        position: relative;

        /* optional glass effect for readability */
        background: rgba(0,0,0,.30);
        border-radius: 16px;
        backdrop-filter: blur(6px);
    }

    .chatbot {
        height: 480px !important;
        overflow-y: auto;
    }

    @media (max-width: 720px){
        :root { --chat-w: 92vw; }
        .gradio-container { margin-left: 4vw; }
    }
""") as demo:

    gr.Markdown(
        """
        <div style='text-align: left; padding: 10px;'>
            <h1 style='font-size: 2.0em; margin-bottom: 0.2em;'><span style='color: #4F46E5;'>kRISHNA.ai</span></h1>
            <p style='font-size: 1.0em; color: #bbb;'>5000-Years of Ancient WISDOM with Modern AI ✨</p>
        </div>
        """,
        elem_id="header"
    )

    gr.ChatInterface(
        fn=gradio_fn,
        examples=[
            "Hello!",
            "How can I overcome fear of failure?",
            "How do I forgive someone who hurt me deeply?",
            "What can I do to stop overthinking?"
        ],
        chatbot=gr.Chatbot(type="messages", elem_classes="chatbot"),
        type="messages",
    )

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