app.py

import streamlit as st
from llama_cpp import Llama
import os
import json
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|>"
    prompt = prompt[:-10]
    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
)

# Set up Streamlit App Layout
st.title("Physics Master Chatbot")
st.markdown("Ask **Physics Master** any physics-related question.")

# Initialize chat history in session state
if 'messages' not in st.session_state:
    st.session_state.messages = [
        {
            'role': 'system',
            'content': 'You are a professional physics master. Answer physics questions directly without using any external resources.'
        }
    ]
    st.session_state.chat_time = time.time()

# Display chat history
for message in st.session_state.messages:
    if message['role'] == 'user':
        st.write(f"**You:** {message['content']}")
    else:
        st.write(f"**Physics Master:** {message['content']}")

# Use a form to manage user input and submission
with st.form(key="input_form", clear_on_submit=True):
    user_input = st.text_input("Ask a question", key="user_input")
    submit_button = st.form_submit_button(label="Send")

if submit_button and user_input:
    # Append user message
    user_message = {'role': 'user', 'content': user_input}
    st.session_state.messages.append(user_message)

    # Prepare to get the response from Physics Master
    st.write('Physics Master is thinking...')

    # Initialize an empty string to accumulate the response
    full_response = ""

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

    for chunk in response:
        delta = chunk['choices'][0]['delta']
        if 'role' in delta:
            st.session_state.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
    st.session_state.messages[-1]['content'] = full_response

    # Display the full response as a paragraph
    st.write(f"**Physics Master:** {full_response}")

# Save the chat history to a JSON file
with open('chat_history.json', 'w', encoding='utf8') as file:
    json.dump(st.session_state.messages, file, indent=4)