import streamlit as st
from streamlit_chat import message
from streamlit_extras.colored_header import colored_header
from streamlit_extras.add_vertical_space import add_vertical_space
from transformers import AutoTokenizer, AutoModelForCausalLM

st.set_page_config(page_title="Einfach.HugChat")

# List of models
models = ["vicuna-13b", "koala-13b", "oasst-pythia-12b", "RWKV-4-Raven-14B", 
          "alpaca-13b", "chatglm-6b", "llama-13b", "dolly-v2-12b", "stablelm-tuned-alpha-7b", 
          "fastchat-t5-3b", "mpt-7b-chat"]

# Sidebar contents
with st.sidebar:
    st.title('EinfachChat')
    st.markdown('''
    ## About
    This app is a LLM-powered chatbot built using:
    - [Streamlit](https://streamlit.io/)
    - [OpenAssistant/oasst-sft-6-llama-30b-xor](https://huggingface.co./OpenAssistant/oasst-sft-6-llama-30b-xor) LLM model
    
    💡 Note: No API key required!
    ''')
    model_name = st.selectbox('Choose a model', models)
    add_vertical_space(5)
    st.write('Made with ❤️ by EinfachAlex')

# Generate empty lists for generated and past.
## generated stores AI generated responses
if 'generated' not in st.session_state:
    st.session_state['generated'] = ["Hallo, wie kann ich dir helfen ?"]
## past stores User's questions
if 'past' not in st.session_state:
    st.session_state['past'] = ['Hi!']

# Layout of input/response containers
input_container = st.container()
colored_header(label='', description='', color_name='blue-30')
response_container = st.container()

# User input
## Function for taking user provided prompt as input
def get_text():
    input_text = st.text_input("You: ", "", key="input")
    return input_text
## Applying the user input box
with input_container:
    user_input = get_text()

# Response output
## Function for taking user prompt as input followed by producing AI generated responses
def generate_response(prompt, model_name):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    inputs = tokenizer(prompt, return_tensors='pt')
    outputs = model.generate(**inputs)
    response = tokenizer.decode(outputs[0])
    return response

## Conditional display of AI generated responses as a function of user provided prompts
with response_container:
    if user_input:
        response = generate_response(user_input, model_name)
        st.session_state.past.append(user_input)
        st.session_state.generated.append(response)
        
    if st.session_state['generated']:
        for i in range(len(st.session_state['generated'])):
            message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
            message(st.session_state["generated"][i], key=str(i))