Spaces:
Sleeping
Sleeping
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 | |
map_id = { | |
0: "sadness", | |
1: "anger", | |
2: "love", | |
3: "surprise", | |
4: "fear", | |
5: "joy" | |
} | |
train = pd.read_csv('train.csv') | |
tokenizer = Tokenizer() | |
tokenizer.fit_on_texts(train.text) | |
model = tf.keras.models.load_model('DETECTION.h5') | |
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}") |