import streamlit as st
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import pandas as pd
import tensorflow as tf

# title
st.title('Ravens AI')

# text input with label
sequence = st.text_input('Enter Amino Acid Sequence')

model_type = st.radio(
    "Choose Linear Epitope Classifier",
    ('Linear T-Cells (MHC Class I Restriction)', 'Linear T-Cells (MHC Class II Restriction)', 'Linear B-Cell'))

# windows length slider
# length = st.slider('Window Length', 1, 50, 10)
threshold = st.slider('Probability Threshold', 0.0, 1.0, 0.5)

model_checkpoint = "facebook/esm2_t6_8M_UR50D"

tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

# try:
if model_type == 'Linear T-Cells (MHC Class I Restriction)':
    try:
        model = TFAutoModelForSequenceClassification.from_pretrained('classifier')
    except:
        st.warning("We're experiencing server issues. Please try again later!", icon="⚠️")
elif model_type == 'Linear T-Cells (MHC Class II Restriction)':
    try:
        model = TFAutoModelForSequenceClassification.from_pretrained('classifier2')
    except:
        st.warning("We're experiencing server issues. Please try again later!", icon="⚠️")
elif model_type == 'Linear B-Cell':
    try:
        model = TFAutoModelForSequenceClassification.from_pretrained('bcell')
    except:
        st.warning("We're experiencing server issues. Please refresh and try again!", icon="⚠️")
try:
    # submit button
    if st.button('Submit'):
        locations = []
        peptide_name = sequence
        peptide = tokenizer(peptide_name, return_tensors="tf")
        output = model(peptide)
        locations.append([peptide_name, output.logits.numpy()[0][0]])

        locations = pd.DataFrame(locations, columns = ['Peptide', 'Probability'])

        # display table with sequence and probability as the headers
        def color_survived(x: float): # x between 0 and 1
            # red to green scale based on x
            # 0 -> red
            # 0.5 -> clear
            # 1 -> green

            # red
            if x < threshold:
                r = 179
                g = 40
                b = 2
            # green
            else:
                r = 18
                g = 150
                b = 6

            return f'background-color: rgb({r}, {g}, {b})'

        st.table(locations.style.applymap(color_survived, subset=['Probability']))
except NameError:
    st.warning("We're experiencing server issues. Please refresh and try again!", icon="⚠️")
# except InvalidArgumentError:
#     st.warning("We're experiencing server issues. Please try again later!", icon="⚠️")