app.py

import gradio as gr
from llama_cpp import Llama
from huggingface_hub import hf_hub_download

# Model identifier from Hugging Face
model_repo = "ID2223-Lab/llama_lora_merged_GGUF"  # Hugging Face model ID

# Download the GGUF file from Hugging Face
model_path = hf_hub_download(repo_id=model_repo, filename="FineTune_Llama.gguf")

# Load the GGUF model using llama-cpp-python
print("Loading model...")
llm = Llama(model_path=model_path, n_ctx=2048, n_threads=8)  # Adjust threads as needed
print("Model loaded!")
# Function for inference
def chat_with_model(user_input, chat_history):
    """
    Process user input and generate a response from the model.
    :param user_input: User's input string
    :param chat_history: List of [user_message, ai_response] pairs
    :return: Updated chat history
    """
    # Construct the prompt from chat history
    prompt = ""
    for user, ai in chat_history:
        prompt += f"User: {user}\nAI: {ai}\n"
    prompt += f"User: {user_input}\nAI:"  # Add the latest user input

    # Generate response from the model
    raw_response = llm(prompt)["choices"][0]["text"].strip()

    # Clean the response (remove extra tags, if any)
    response = raw_response.split("User:")[0].strip()

    # Update chat history with the new turn
    chat_history.append((user_input, response))
    return chat_history, chat_history


# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# 🦙 LLaMA Chatbot with Base Model and LoRA Adapter")
    chatbot = gr.Chatbot(label="Chat with the Model")

    with gr.Row():
        with gr.Column(scale=4):
            user_input = gr.Textbox(label="Your Message", placeholder="Type a message...")
        with gr.Column(scale=1):
            submit_btn = gr.Button("Send")

    chat_history = gr.State([])

    # Link components
    submit_btn.click(
        chat_with_model,
        inputs=[user_input, chat_history],
        outputs=[chatbot, chat_history],
        show_progress=True,
    )

# Launch the Gradio app
demo.launch()