app.py

from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr

# Define model name
MODEL_NAME = "jojo-ai-mst/MyanmarGPT-Chat"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    torch_dtype="float32",  # Optimized for CPU usage
    low_cpu_mem_usage=True  # Helps with limited memory
)

# Chatbot function
def chatbot(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")  # Tokenize the input text
    outputs = model.generate(
        inputs.input_ids,
        max_new_tokens=150,  # Limit response length
        temperature=0.7,     # Control randomness
        top_p=0.9            # Nucleus sampling
    )
    # Decode and return the generated text
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Gradio interface
interface = gr.Interface(
    fn=chatbot,
    inputs=gr.Textbox(
        label="Chat with Burmese ChatGPT",
        placeholder="Type your message here in Burmese...",
        lines=5
    ),
    outputs=gr.Textbox(label="Response"),
    title="Burmese ChatGPT",
    description="A chatbot powered by MyanmarGPT-Chat for Burmese conversations."
)

# Launch the interface
if __name__ == "__main__":
    interface.launch()