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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_NAME = "ValiantLabs/Qwen3-4B-Thinking-2507-Esper3.1"
# Load model & tokenizer once at startup
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
torch_dtype="auto",
device_map="auto"
)
def ask_question(prompt):
"""Generate response (thinking + final content) from Qwen3 model."""
try:
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # thinking mode
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**inputs,
max_new_tokens=4096,
temperature=0.7,
do_sample=True
)
output_ids = generated_ids[0][len(inputs.input_ids[0]):].tolist()
# Find the thinking section (token 151668 == </think>)
try:
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
return thinking_content, content
except Exception as e:
return f"⚠️ Error: {e}", ""
# --- Gradio UI ---
with gr.Blocks(title="Qwen3 Thinking Chat") as demo:
gr.Markdown("## 🧠 Qwen3-4B-Thinking — Ask Anything")
gr.Markdown(
"This demo uses **ValiantLabs/Qwen3-4B-Thinking-2507-Esper3.1**, "
"a reasoning model that shows its internal 'thinking' trace before giving the final answer."
)
with gr.Row():
prompt_box = gr.Textbox(
label="Ask your question",
placeholder="e.g. Explain how quantum entanglement works.",
lines=3
)
with gr.Row():
think_output = gr.Textbox(label="🧩 Thinking process", lines=10)
final_output = gr.Textbox(label="💬 Final answer", lines=10)
ask_btn = gr.Button("🚀 Generate Answer")
ask_btn.click(
fn=ask_question,
inputs=prompt_box,
outputs=[think_output, final_output]
)
demo.launch()
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