import streamlit as st
from huggingface_hub import snapshot_download

import os  # utility library

# libraries to load the model and serve inference
import tensorflow_text
import tensorflow as tf


def main():
    st.title("Interactive demo: T5 Multitasking Demo")
    st.write("**Demo for T5's different tasks including machine translation, \
        text summarization, document similarity, and grammatical correctness of sentences.**")
    saved_model_path = load_model_cache()

    # Model is loaded in st.session_state to remain stateless across reloading
    if 'model' not in st.session_state:
        st.session_state.model = tf.saved_model.load(saved_model_path, ["serve"])

    dashboard(st.session_state.model)


@st.cache
def load_model_cache():
    """Function to retrieve the model from HuggingFace Hub and cache it using st.cache wrapper
    """
    CACHE_DIR = "hfhub_cache"  # where the library's fork would be stored once downloaded
    if not os.path.exists(CACHE_DIR):
        os.mkdir(CACHE_DIR)

    # download the files from huggingface repo and load the model with tensorflow
    snapshot_download(repo_id="stevekola/T5", cache_dir=CACHE_DIR)
    saved_model_path = os.path.join(CACHE_DIR, os.listdir(CACHE_DIR)[0])
    return saved_model_path
        

def dashboard(model):
    """"Function to display the inputs and results
    params:
        model   stateless model to run inference from
    """
    st.sidebar.write("**Select the Task Type over here**")
    task_type = st.sidebar.radio("Task Type",
                                 [
                                  "Translate English to French",
                                  "Translate English to German",
                                  "Translate English to Romanian",
                                  "Grammatical Correctness of Sentence",
                                  "Text Summarization",
                                  "Document Similarity Score"
                                 ])
    if task_type.startswith("Document Similarity"): # document similarity requires two documents
        sentence1 = st.text_area("The first document/sentence",
                            "I reside in the commercial capital city of Nigeria, which is Lagos.")
        sentence2 = st.text_area("The second document/sentence",
                            "I live in Lagos.")
        sentence = sentence1 + "---" + sentence2
    elif task_type.startswith("Text Summarization"): # text summarization's default input should be longer
        sentence = st.text_area("Input sentence",
                                "I don't care about those doing the comparison, but comparing the Ghanaian Jollof Rice \
                                to Nigerian Jollof Rice is an insult to Nigerians.")
    else:
        sentence = st.text_area("Input sentence",
                                "I am Steven and I live in Lagos, Nigeria.")

    st.write("**Output Text**")
    with st.spinner("Please wait..."): # spinner while model is running inferences
        output_text = predict(task_type, sentence, model)
        st.write(output_text)
        # st.download_button("Download output text", output_text)  # download_button is yet to be production-ready
    

def predict(task_type, sentence, model):
    """Function to parse the user inputs, run the parsed text through the
    model and return output in a readable format.
    params:
        task_type   sentence representing the type of task to run on T5 model
        sentence    sentence to get inference on
        model       model to get inferences from
    returns:
        text decoded into a human-readable format.
    """
    task_dict = {
        "Translate English to French": "Translate English to French",
        "Translate English to German": "Translate English to German",
        "Translate English to Romanian": "Translate English to Romanian",
        "Grammatical Correctness of Sentence": "cola sentence",
        "Text Summarization": "summarize",
        "Document Similarity Score": "stsb",
    }
    question = f"{task_dict[task_type]}: {sentence}"  # parsing the user inputs into a format recognized by T5
    # Document Similarity takes in two sentences so it has to be parsed in a separate manner
    if task_type.startswith("Document Similarity"):
        sentences = sentence.split('---')
        question = f"{task_dict[task_type]} sentence1: {sentences[0]} sentence2: {sentences[1]}"
    return predict_fn([question], model)[0].decode('utf-8')


def predict_fn(x, model):
    """Function to get inferences from model on live data points.
    params:
        x       input text to run get output on
        model   model to run inferences from
    returns:
        a numpy array representing the output
    """
    return model.signatures['serving_default'](tf.constant(x))['outputs'].numpy()


if __name__ == "__main__":
    main()