import streamlit as st  # Web App
from main import classify

#demo_phrases = """ Here are some examples:
#this is a phrase
#is it neutral
#nothing else to say
#man I'm so damn angry
#sarcasm lol
#I love this product
#"""
demo_phrases = pd.read_csv('./train.csv')['comment_text'].head(20).astype(str).str.cat(sep='\n')
# title
st.title("Sentiment Analysis")

# subtitle
st.markdown("## A selection of popular sentiment analysis models -  hosted on 🤗 Spaces")

model_name = st.selectbox(
    "Select a pre-trained model",
    [
        "finiteautomata/bertweet-base-sentiment-analysis",
        "ahmedrachid/FinancialBERT-Sentiment-Analysis",
        "finiteautomata/beto-sentiment-analysis",
        "NativeVex/custom-fine-tuned"
    ],
)

input_sentences = st.text_area("Sentences", value=demo_phrases, height=200)

data = input_sentences.split("\n")

from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_path = "bin/model4"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

from typing import List
import torch
import numpy as np
import pandas as pd

def infer(text: str) -> List[float]:
    encoding = tokenizer(text, return_tensors="pt")
    encoding = {k: v.to(model.device) for k,v in encoding.items()}
    outputs = model(**encoding)
    logits = outputs.logits
    sigmoid = torch.nn.Sigmoid()
    probs = sigmoid(logits.squeeze().cpu())
    predictions = np.zeros(probs.shape)
    predictions[np.where(probs >= 0.5)] = 1
    predictions = pd.Series(predictions == 1)
    predictions.index = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
    return {"label": str(predictions), "score": str(probs)}


def wrapper(*args, **kwargs):
    if args[0] != "NativeVex/custom-fine-tuned":
        return classify(*args, **kwargs)
    else:
        return infer(text=args[1])

    

if st.button("Classify"):
    st.write("Please allow a few minutes for the model to run/download")
    for i in range(len(data)):
        j = wrapper(model_name.strip(), data[i])[0]
        sentiment = j["label"]
        confidence = j["score"]
        st.write(
            f"{i}. {data[i]} :: Classification - {sentiment} with confidence {confidence}"
        )


st.markdown(
    "Link to the app - [image-to-text-app on 🤗 Spaces](https://huggingface.co/spaces/Amrrs/image-to-text-app)"
)