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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/Nizami-1.7B").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
# --- UI Design ---
with gr.Blocks(theme=gr.themes.Soft(primary_hue="yellow", secondary_hue="slate")) as demo:
gr.HTML("""
<div style="text-align: center; margin-bottom: 20px;">
<h1 style="font-family: 'Inter', sans-serif; font-weight: 800; color: #FACC15; font-size: 2.2em;">
📚 Nizami-1.7B
</h1>
<p style="color: #FDE047; font-size: 1.05em; margin-top: -10px;">
Academic style comprehension and reasoning in Azerbaijani.
</p>
</div>
""")
with gr.Row():
with gr.Column(scale=6):
chatbot = gr.Chatbot(
label="Academic-style Chat",
height=600,
bubble_full_width=True,
show_copy_button=True,
avatar_images=(
"https://cdn-icons-png.flaticon.com/512/1077/1077012.png", # user icon
"https://cdn-icons-png.flaticon.com/512/4140/4140048.png", # bot icon
),
)
user_input = gr.Textbox(
placeholder="Ask me...",
label="💬 Your question",
lines=3,
autofocus=True,
)
with gr.Row():
send_btn = gr.Button("🚀 Send", variant="primary")
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])
gr.HTML("""
<div style="text-align: center; margin-top: 25px; color: #6B7280; font-size: 0.9em;">
Powered by <b>Qwen3-1.7B + Nizami-1.7B</b> | Built with ❤️ using Gradio
</div>
""")
demo.launch(share=True) |