app.py

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

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

# --- System prompt (Gita persona) ---
GITA_SYSTEM_PROMPT = """You are KRISHNA.ai — a compassionate, serene, and practical guide inspired by the Bhagavad Gita.
Style: calm, clear, inclusive, and down-to-earth. Use everyday language, avoid jargon.
When fitting, quote a brief shloka with Chapter:Verse (e.g., 2:47) and give a one-line meaning. Do not over-quote.
Emphasize: selfless action (karma-yoga), equanimity, disciplined mind, devotion, and wisdom — applicable to modern life.
Be non-sectarian and respectful of all beliefs. If a topic is clinical/medical/legal, gently suggest professional help.
Prefer concise replies (5–10 sentences). Use short steps/bullets for “how-to” answers. End with a one-line “Essence:” summary when helpful."""

# 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="""
    .gradio-container {
        max-width: 600px;
        margin: auto;
        padding: 20px;
        font-family: sans-serif;
        position: relative;
        }
    .chatbot {
        height: 500px !important;
        overflow-y: auto;
        }
    .corner {
       position: fixed;
       bottom: 2px;
       z-index: 9999;
       pointer-events: none;
        } 
    #left { left: 2px; }
    #right { right: 2px; }
    .corner img {
       height: 500px;  /* fixed height */
       width: auto;    /* auto to keep aspect ratio */
        }
    
    """) as demo:
    gr.Markdown(
    """
        <div style='text-align: center; padding: 10px;'>
        <h1 style='font-size: 2.2em; margin-bottom: 0.2em;'><span style='color: #4F46E5;'>kRISHNA.ai</span></h1>
        <p style='font-size: 1.1em; color: #555;'>5000-Years of Ancient WISDOM with Modern AI ✨</p>
        </div>
    """,
    elem_id="header"
    )
    chat = 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",
    )
    gr.HTML(f"""
      <div id="left" class="corner">
        <img src="" alt="Arjun">
      </div>
      <div id="right" class="corner">
        <img src="" alt="Krishna">
      </div>
    """)

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