app.py

from llama_cpp import Llama
import gradio as gr


llm = Llama.from_pretrained(
	repo_id="rcarioniporras/model_modelcentric_llama_gguf",
	filename="unsloth.Q4_K_M.gguf",
)


def predict(message, history):
    messages = [{"role": "system", "content": "You are a helpful assistant who answers questions in a concise but thorough way. Prioritize clarity and usefulness in all interactions."}]
    for user_message, bot_message in history:
        if user_message:
            messages.append({"role": "user", "content": user_message})
        if bot_message:
            messages.append({"role": "assistant", "content": bot_message})
    messages.append({"role": "user", "content": message})
    
    response = ""
    for chunk in llm.create_chat_completion(
        stream=True,
        messages=messages,
    ):
        part = chunk["choices"][0]["delta"].get("content", None)
        if part:
            response += part
        yield response

conversation_starters = [
    {"text": "What is object-oriented programming (OOP), and what are its four main principles?"},
    {"text": "Compare the stack and queue data structures."},
    {"text": "Simplify the expression 3(x+4)−2(2x−1)."},
    {"text": "What are some fun facts about space?"}
]

demo = gr.ChatInterface(fn=predict,  theme="Shivi/calm_seafoam")
                        
if __name__ == "__main__":
    demo.launch()