app.py

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

# ✅ Load token from Hugging Face secret
HF_TOKEN = os.environ.get("HF_TOKEN")

# ✅ Model ID
model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"

# ✅ Load tokenizer and model securely
tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    use_auth_token=HF_TOKEN,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto"
)

# 🧠 Prompt Builder
def build_prompt(user_input, history):
    prompt = "You are a pirate chatbot who always responds in pirate speak!\n"
    for user_msg, bot_reply in history:
        prompt += f"User: {user_msg}\nPirate: {bot_reply}\n"
    prompt += f"User: {user_input}\nPirate:"
    return prompt

# 💬 Chat Handler
def chat(user_input, history):
    prompt = build_prompt(user_input, history)
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=True,
        temperature=0.8,
        top_p=0.9,
        pad_token_id=tokenizer.eos_token_id
    )

    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    pirate_reply = response.split("Pirate:")[-1].strip()
    return pirate_reply

# 🧱 Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 🏴‍☠️ Talk to the Pirate Bot!")
    chatbot = gr.Chatbot()
    msg = gr.Textbox(placeholder="Ask the pirate something...", label="Your Message")
    clear = gr.Button("Clear Conversation")
    history = gr.State([])

    def respond(user_input, history):
        response = chat(user_input, history)
        history.append((user_input, response))
        return history, history

    msg.submit(respond, [msg, history], [chatbot, history])
    clear.click(lambda: ([], []), None, [chatbot, history])

demo.launch()