app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# --- Load tokenizer and model for CPU ---
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-1.7B")

base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/Qwen3-1.7B",
    dtype=torch.float32,
    device_map={"": "cpu"},
)

model = PeftModel.from_pretrained(base_model, "khazarai/Qwen3-ALP-AZ").to("cpu")

# --- Chatbot logic ---
def generate_response(user_input, chat_history):
    if not user_input.strip():
        return chat_history, chat_history

    chat_history.append({"role": "user", "content": user_input})

    text = tokenizer.apply_chat_template(
        chat_history,
        tokenize=False,
        add_generation_prompt=True,
        enable_thinking=False,
    )

    inputs = tokenizer(text, return_tensors="pt").to("cpu")

    output_tokens = model.generate(
        **inputs,
        max_new_tokens=1024,
        temperature=0.7,
        top_p=0.8,
        top_k=20,
        do_sample=True
    )

    response = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
    response = response.split(user_input)[-1].strip()

    chat_history.append({"role": "assistant", "content": response})

    gr_chat_history = [
        (m["content"], chat_history[i + 1]["content"])
        for i, m in enumerate(chat_history[:-1])
        if m["role"] == "user"
    ]

    return gr_chat_history, chat_history


# --- Advanced UI Design ---
with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="slate")) as demo:
    gr.HTML("""
    <style>
        body {background: radial-gradient(circle at top, #E9D5FF 0%, #F5F3FF 100%);}
        .gradio-container {font-family: 'Inter', sans-serif;}
        .chat-header {
            text-align: center;
            background: linear-gradient(90deg, #C084FC, #A855F7);
            color: white;
            padding: 20px 10px;
            border-radius: 18px;
            margin-bottom: 20px;
            box-shadow: 0px 4px 20px rgba(168,85,247,0.3);
        }
        .chat-header h1 {
            font-size: 2.4em;
            font-weight: 800;
            margin-bottom: 0px;
        }
        .chat-header p {
            margin-top: 5px;
            color: #F3E8FF;
            font-weight: 500;
        }
        .send-btn {
            background: linear-gradient(90deg, #C084FC, #A855F7);
            color: white !important;
            transition: all 0.25s ease-in-out;
        }
        .send-btn:hover {
            transform: scale(1.05);
            box-shadow: 0 0 12px rgba(192,132,252,0.5);
        }
        .textbox {
            backdrop-filter: blur(12px);
            background-color: rgba(255,255,255,0.6);
            border-radius: 16px !important;
        }
        .footer {
            text-align: center;
            margin-top: 25px;
            color: #6B7280;
            font-size: 0.9em;
        }
    </style>
    <div class="chat-header">
        <h1> 🧠 Azerbaijani Chatbot </h1>
    </div>
    """)

    with gr.Row():
        with gr.Column(scale=6):
            chatbot = gr.Chatbot(
                label="💬 Chat-az",
                height=600,
                bubble_full_width=True,
                show_copy_button=True,
                avatar_images=(
                    "https://cdn-icons-png.flaticon.com/512/1077/1077012.png",  # user
                    "https://cdn-icons-png.flaticon.com/512/4140/4140048.png",  # bot
                ),
            )

            user_input = gr.Textbox(
                placeholder="Ask about..",
                label="Type your question",
                lines=3,
                elem_classes=["textbox"],
                autofocus=True,
            )

            with gr.Row():
                send_btn = gr.Button("🚀 Send", variant="primary", elem_classes=["send-btn"])
                clear_btn = gr.Button("🧹 Clear Chat")

    state = gr.State([])

    send_btn.click(generate_response, [user_input, state], [chatbot, state])
    user_input.submit(generate_response, [user_input, state], [chatbot, state])
    clear_btn.click(lambda: ([], []), None, [chatbot, state])

demo.launch(share=True)