import streamlit as st
import gradio as gr
import shap
import numpy as np
import scipy as sp
import torch
import tensorflow as tf
import transformers
from transformers import RobertaTokenizer, RobertaModel
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
from transformers_interpret import SequenceClassificationExplainer

tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/ADRv1")  
model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1")
modelc = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1").cuda


cls_explainer = SequenceClassificationExplainer(
    model,
    tokenizer)

# define a prediction function
def f(x):
    tv = torch.tensor([tokenizer.encode(v, padding='max_length', max_length=500, truncation=True) for v in x]).cuda()
    outputs = modelc(tv)[0].detach().cpu().numpy()
    scores = (np.exp(outputs).T / np.exp(outputs).sum(-1)).T
    val = sp.special.logit(scores[:,1]) # use one vs rest logit units
    return val

def adr_predict(x):
    encoded_input = tokenizer(x, return_tensors='pt')
    output = model(**encoded_input)
    scores = output[0][0].detach().numpy()
    scores = tf.nn.softmax(scores)
    
    # # build a pipeline object to do predictions
    # pred = transformers.pipeline("text-classification", model=model, 
    #                          tokenizer=tokenizer, device=0, return_all_scores=True)
    # explainer = shap.Explainer(pred)
    # shap_values = explainer([x])
    # shap_plot = shap.plots.text(shap_values)

    word_attributions = cls_explainer(str(x))
    letter = []
    score = []
    for i in word_attributions:
        if i[1]>0.5:
            a = "++"
        elif (i[1]<=0.5) and (i[1]>0.1):
            a = "+"
        elif (i[1]>=-0.5) and (i[1]<-0.1):
            a = "-"
        elif i[1]<-0.5:
            a = "--"
        else: 
            a = "NA"
    
        letter.append(i[0])
        score.append(a)
    
    word_attributions = [(letter[i], score[i]) for i in range(0, len(letter))]

    # SHAP:
    # build an explainer using a token masker
    explainer = shap.Explainer(f, tokenizer)
    shap_values = explainer(str(x), fixed_context=1)
    # plot the first sentence's explanation
    plt = shap.plots.text(shap_values[0],display=False)


    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, word_attributions,plt

def main(text):
    text = str(text).lower()
    obj = adr_predict(text)
    return obj[0],obj[1],obj[2]

title = "Welcome to **ADR Detector** 🪐"
description1 = """
This app takes text (up to a few sentences) and predicts to what extent the text describes severe (or non-severe) adverse reaction to medicaitons.
"""

with gr.Blocks(title=title) as demo:
    gr.Markdown(f"## {title}")
    gr.Markdown(description1)
    gr.Markdown("""---""")
    text = gr.Textbox(label="Enter Your Text Here:",lines=2, placeholder="Type it here ...")
    submit_btn = gr.Button("Analyze")


    with gr.Column(visible=True) as output_col:
        label = gr.Label(label = "Predicted Label")
        # impplot = gr.HighlightedText(label="Important Words", combine_adjacent=False).style(
        # color_map={"+++": "royalblue","++": "cornflowerblue",
        #  "+": "lightsteelblue", "NA":"white"})
        # NER = gr.HTML(label = 'NER:')
        intp = gr.HighlightedText(label="Word Scores",
        combine_adjacent=False).style(color_map={"++": "darkred","+": "red", 
                                                "--": "darkblue",
                                                "-": "blue", "NA":"white"})

        shap = gr.HighlightedText(label="SHAP Scores",combine_adjacent=False)


    submit_btn.click(
        main,
        [text],
        [label,intp,shap], api_name="adr"
    )

    gr.Markdown("### Click on any of the examples below to see to what extent they contain resilience messaging:")
    gr.Examples([["I have minor pain."],["I have severe pain."]], [text], [label,intp,shap], main, cache_examples=True)
    
demo.launch()