app.py

import streamlit as st
import pandas as pd
import numpy as ny
import tensorflow as tf
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.preprocessing import LabelEncoder
from keras.layers import *
from keras import Model

map_id = {
  0: "sadness",
  1: "anger",
  2: "love",
  3: "surprise",
  4: "fear",
  5: "joy"
}

train = pd.read_csv('train.csv')
for index, row in train.iterrows():
  row['emotion'] = map_emotion[row['emotion']]
tokenizer = Tokenizer()
tokenizer.fit_on_texts(train.text)

model = tf.keras.models.load_model('DETECTION.keras')
class Predict:
    def __init__(self, model, tokenizer):
        self.model = model
        self.tokenizer = tokenizer
    
    def predict(self, txt):
        x = pad_sequences(self.tokenizer.texts_to_sequences([txt]), maxlen=30)
        x = self.model(x)
        x = ny.argmax(x)
        return map_id[x]
predict = Predict(model, tokenizer)


st.title("TONE DETECTION | BCS WINTER PROJECT")
st.write("Enter a sentence to analyze text's Tone:")

user_input = st.text_input("")
if user_input:
    result = predict.predict(user_input)
    st.write(f"TONE: {result}")