app.py

import streamlit as st
from llama_cpp import Llama

st.set_page_config(page_title="russian medical phi3-mini", page_icon="🤖", layout="wide")

# Custom CSS for better styling
st.markdown("""
<style>
.stTextInput > div > div > input {
    background-color: #f0f2f6;
}
.chat-message {
    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
}
.chat-message.user {
    background-color: #2b313e
}
.chat-message.bot {
    background-color: #475063
}
.chat-message .avatar {
  width: 20%;
}
.chat-message .avatar img {
  max-width: 78px;
  max-height: 78px;
  border-radius: 50%;
  object-fit: cover;
}
.chat-message .message {
  width: 80%;
  padding: 0 1.5rem;
  color: #fff;
}
.chat-message.user::before {
    content: "👤";
    font-size: 1.5em;
    margin-right: 0.5em;
}

.chat-message.bot::before {
    content: "🤖";
    font-size: 1.5em;
    margin-right: 0.5em;
}
</style>
""", unsafe_allow_html=True)

@st.cache_resource
def load_model():
    return Llama.from_pretrained(
        repo_id="Mykes/med_phi3-mini-4k-GGUF",
        filename="*Q4_K_M.gguf",
        verbose=False,
        n_ctx=512,
        n_batch=256,
        n_threads=8,
        use_mlock=True,
        use_mmap=True,
        # n_ctx=256,  # Reduced context window
        # n_batch=8,  # Smaller batch size
        # n_threads=2,  # Adjust based on your CPU cores
        # use_mmap=True,
    )

llm = load_model()

def format_context(messages):
    context = ""
    for message in messages:
        if message["role"] == "user":
            context += f"Human: {message['content']}\n"
        else:
            context += f"Assistant: {message['content']}\n"
    return context

# Sidebar
st.sidebar.title("Chat with AI")
st.sidebar.markdown("This is a simple chat interface using Streamlit and an AI model.")

# # Add useful information to the sidebar
# st.sidebar.header("How to use")
# st.sidebar.markdown("""
# 1. Type your question in the chat input box at the bottom of the screen.
# 2. Press Enter or click the Send button to submit your question.
# 3. The AI will generate a response based on your input.
# 4. You can have a continuous conversation by asking follow-up questions.
# """)

st.sidebar.header("Model Information")
st.sidebar.markdown("""
- Model: med_phi3-mini-4k-GGUF 
  ([View on Hugging Face](https://huggingface.co/Mykes/med_phi3-mini-4k-GGUF))
- Context Length: 512 tokens
- This model is specialized in medical knowledge.
- Russian language
""")

st.sidebar.header("Tips")
st.sidebar.markdown("""
- Be clear and specific in your questions.
- For medical queries, provide relevant details.
- Remember that this is an AI model and may not always be 100% accurate.
""")

# Main chat interface
st.title("russian medical phi3-mini")

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# React to user input
if prompt := st.chat_input("What is your question?"):
    # Display user message in chat message container
    st.chat_message("user").markdown(prompt)
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})

    # Format the context with the last 5 messages
    context = format_context(st.session_state.messages[-3:])
    
    # Prepare the model input
    model_input = f"{context}Human: {prompt}\nAssistant:"
    
    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        message_placeholder = st.empty()
        full_response = ""
        
        for token in llm(
            model_input,
            max_tokens=None,
            stop=["Human:", "<end_of_turn>"],
            echo=True,
            stream=True
        ):
            full_response += token['choices'][0]['text']
            message_placeholder.markdown(full_response + "▌")
        
        # Remove the initial context and prompt from the response
        assistant_response = full_response.split("Assistant:")[-1].strip()
        message_placeholder.markdown(assistant_response)
    
    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": assistant_response})

# Add a button to clear the chat history
if st.sidebar.button("Clear Chat History"):
    st.session_state.messages = []
    st.experimental_rerun()

# Display the number of messages in the current conversation
st.sidebar.markdown(f"Current conversation length: {len(st.session_state.messages)} messages")

# Add a footer
st.sidebar.markdown("---")
st.sidebar.markdown("Created with ❤️ using Streamlit and Llama.cpp")