helpers/chat.py

import streamlit as st
import logging
from huggingface_hub import InferenceClient
from helpers.systemPrompts import base, tutor
import os
logger = logging.getLogger(__name__)

api_key = os.environ.get('hf_api')
client = InferenceClient(api_key=api_key)

def hf_generator(model,prompt,data,system=None):
    if system:    
        messages = [
            {
                "role": "system",
                "content": [
                    {
                        "type": "text",
                        "text": system
                    }
                ]
            },
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": prompt
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": data
                        }
                    }
                ]
            }
        ]
    else:
                messages = [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": prompt
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": data
                        }
                    }
                ]
            }
        ]

    completion = client.chat.completions.create(
        model=model, 
        messages=messages, 
        max_tokens=500
    )
    response = completion.choices[0].message.content
    logger.info({"role": "assistant", "content": response})
    st.session_state.messages.append({"role": "assistant", "content": response})
    return completion.choices[0].message.content
    

    

def basicChat():
# Accept user input and then writes the response
    if prompt := st.chat_input("How may I help you learn math today?"):
        # Add user message to chat history
        st.session_state.messages.append({"role": "user", "content": prompt})
        logger.info(st.session_state.messages[-1])
        # Display user message in chat message container
        with st.chat_message("user"):
            st.markdown(prompt)

        with st.chat_message(st.session_state.model):
            logger.info(f"""Message to {st.session_state.model}: {[
                    {"role": m["role"], "content": m["content"]}
                    for m in st.session_state.messages
                ]}""")
            response = st.write_stream(hf_generator(
                st.session_state.model,
                [
                    {"role": m["role"], "content": m["content"]}
                    for m in st.session_state.messages
                ]
            ))
            st.session_state.messages.append({"role": "assistant", "content": response})
            logger.info(st.session_state.messages[-1])


def mmChat(data):
    if prompt := st.chat_input("How may I help you learn math today?"):
         # Add user message to chat history
        st.session_state.messages.append({"role": "user", "content": prompt,"images":[data]})
        logger.info(st.session_state.messages[-1])
        # Display user message in chat message container
        with st.chat_message("user"):
            st.markdown(prompt)

        with st.chat_message(st.session_state.model):
            logger.info(f"Message to {st.session_state.model}: {st.session_state.messages[-1]}")
            response = st.write(hf_generator(
                st.session_state.model,
                prompt,
                data))
            st.session_state.messages.append({"role": "assistant", "content": response})
            logger.info(st.session_state.messages[-1])


def guidedMM(sysChoice:str, data):
    if sysChoice == "Tutor":
        system = tutor
    else:
        system = base
    
    if prompt := st.chat_input("How may I help you learn math today?"):
         # Add user message to chat history
        st.session_state.messages.append({"role": "user", "content": prompt,"images":[data]})
        # Display user message in chat message container
        with st.chat_message("user"):
            st.markdown(prompt)

        with st.chat_message(st.session_state.model):
            logger.info(f"Message to {st.session_state.model}: {st.session_state.messages[-1]}")
            response = st.write(hf_generator(
                st.session_state.model,
                prompt,
                data,
                system
            ))