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8659119 76d1715 d0752cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
# Load tokenizer & model
model_name = "lewishamilton21/Qwen_1.5B_multilingual_Fine-Tuned_LLM"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
# Text generation pipeline
generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
# Chat function
def chat(user_message, history):
# Format prompt from chat history
prompt = ""
for msg in history:
prompt += f"{msg[0]}: {msg[1]}\n"
prompt += f"User: {user_message}\nAI:"
# Generate model response
output = generator(prompt, max_length=512, do_sample=True, temperature=0.7, top_p=0.9, num_return_sequences=1)
reply = output[0]['generated_text'].split("AI:")[-1].strip()
# Update history with new message and reply
history.append(("User", user_message))
history.append(("AI", reply))
return history, history
# Gradio app layout
with gr.Blocks() as demo:
gr.Markdown("# 🗣️ Multilingual Qwen 1.5B Chatbot")
chatbot = gr.Chatbot()
msg = gr.Textbox(label="Type your message here...")
clear = gr.Button("Clear Chat")
state = gr.State([])
msg.submit(chat, [msg, state], [chatbot, state])
clear.click(lambda: ([], []), None, [chatbot, state])
# Run the Gradio app
demo.launch(share=True)
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