app.py

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)