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 pipeline
from transformers import RobertaTokenizer, RobertaModel
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import matplotlib.pyplot as plt

device = "cuda:0" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/ADRv1")  
model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1").to(device)

# build a pipeline object to do predictions
pred = transformers.pipeline("text-classification", model=model, 
                             tokenizer=tokenizer, return_all_scores=True)

explainer = shap.Explainer(pred)

##
classifier = transformers.pipeline("text-classification", model = "cross-encoder/qnli-electra-base")

def med_score(x):
    label = x['label']
    score_1 = x['score']
    return score_1

##

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)
   
    shap_values = explainer([str(x).lower()])
    local_plot = shap.plots.text(shap_values[0], display=False)

    med = med_score(classifier(x+str(", There is a medication."))[0])
   
    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, local_plot, {"Contains Medication": float(med), "No Medications": float(1-med)}


def main(prob1):
    text = str(prob1).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. Please do NOT use for medical diagnosis."""

with gr.Blocks(title=title) as demo:
    gr.Markdown(f"## {title}")
    gr.Markdown(description1)
    gr.Markdown("""---""")
    prob1 = 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")
        local_plot = gr.HTML(label = 'Shap:')
        med = gr.Label(label = "Contains Medication")
        

    submit_btn.click(
        main,
        [prob1],
        [label
         ,local_plot, med
        ], 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 severe pain."],["I have minor pain."]], [prob1], [label,local_plot, med
                                                                          ], main, cache_examples=True)
    
demo.launch()