app.py

import gradio as gr
from llama_cpp import Llama
import json
import os
import time

# Function to convert message history to prompt
def prompt_from_messages(messages):
    prompt = ''
    for message in messages:
        prompt += f"<|start_header_id|>{message['role']}<|end_header_id|>\n\n"
        prompt += f"{message['content']}<|eot_id|>{{}}"  # Corrected here
    prompt = prompt[:-10]  # Adjust the slicing accordingly
    return prompt

# Initialize the Llama model
llm = Llama.from_pretrained(
    repo_id='gallen881/Llama-3-8B-Physics_Master-GGUF',
    filename='unsloth.Q4_K_M.gguf',
    n_ctx=2048,
    verbose=False
)

# Initialize chat history
messages = [
    {
        'role': 'system',
        'content': 'You are a professional physics master. Answer physics questions directly without using any external resources.'
    }
]

# Function to handle user input and generate a response
def chat_with_physics_master(user_input):
    global messages  # Ensure we can modify the global messages variable

    # Append user message
    user_message = {'role': 'user', 'content': user_input}
    messages.append(user_message)

    # Prepare to get the response from Physics Master
    full_response = ""

    # Fetch response tokens and accumulate them
    response = llm.create_chat_completion(
        messages=messages,
        stream=True
    )

    for chunk in response:
        delta = chunk['choices'][0]['delta']
        if 'role' in delta:
            messages.append({'role': delta['role'], 'content': ''})
        elif 'content' in delta:
            token = delta['content']
            # Accumulate tokens into the full response
            full_response += token

    # Once the full response is received, append it to the chat history
    messages[-1]['content'] = full_response

    # Return the entire chat history for display
    return [(msg['role'], msg['content']) for msg in messages]

# Gradio interface
iface = gr.Interface(
    fn=chat_with_physics_master,
    inputs=gr.inputs.Textbox(label="Ask a question"),
    outputs=gr.outputs.Chatbox(label="Chat History"),
    title="Physics Master Chatbot",
    description="Ask **Physics Master** any physics-related question.",
)

# Launch the Gradio app
iface.launch()