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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
model_name = "Qwen/Qwen2.5-3B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Chat history
chat_history = []
# System prompt
SYSTEM_PROMPT = "You are Qwen/Qwen2.5-3B-Instruct, created by Alibaba Cloud. You are a helpful assistant."
def generate_response(user_input, history):
# Build message list
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
for user_msg, bot_msg in history:
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": user_input})
# Apply chat template
prompt_text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
# Tokenize
model_inputs = tokenizer([prompt_text], return_tensors="pt").to(model.device)
# Generate response
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
# Only return new tokens
new_tokens = generated_ids[0][model_inputs.input_ids.shape[-1]:]
response = tokenizer.decode(new_tokens, skip_special_tokens=True)
# Update chat history
history.append((user_input, response))
return history, history
# Launch Gradio Chatbot UI
chatbot_ui = gr.ChatInterface(
fn=generate_response,
title="🧠 Qwen 2.5 3B - Chatbot",
description="A simple chatbot interface powered by Qwen2.5-3B-Instruct (Alibaba Cloud).",
theme="soft",
examples = [
"How can virtual reality (VR) influence consumer behavior towards sustainability?",
"What impact does sustainable packaging have on consumer purchasing decisions?",
"In what ways can education promote more sustainable consumer behaviors?"
],
)
if __name__ == "__main__":
chatbot_ui.launch()
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