Update app.py

app.py

@@ -1,6 +1,7 @@
 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
@@ -15,42 +16,14 @@ map_id = {
   4: "fear",
   5: "joy"
 }
-map_emotion = {
-  "sadness": 0,
-  "anger": 1,
-  "love": 2,
-  "surprise": 3,
-  "fear": 4,
-  "joy": 5
-}
 
 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)
-Length = len(tokenizer.word_index) + 1
-x_train = pad_sequences(tokenizer.texts_to_sequences(train.text), maxlen=30)
-encoder = LabelEncoder()
-encoder.fit(train["emotion"].to_list())
-y_train = encoder.transform(train["emotion"].to_list())
-y_train = y_train.reshape(-1, 1)
-
-embedding_layer = Embedding(Length,
-                           64,
-                           input_length=30)
-
-input_seq = Input(shape=(x_train.shape[1],))
-x = embedding_layer(input_seq)
-x = LSTM(10, return_sequences=True) (x)
-x = Flatten() (x)
-output = Dense(encoder.classes_.shape[0], activation="softmax") (x)
 
-model = Model(input_seq, output)
-model.compile(optimizer='adam',
-             loss="sparse_categorical_crossentropy",
-             metrics=["accuracy"])
-model.fit(x_train, y_train, epochs=20, batch_size=32)
+model = tf.keras.models.load_model('DETECTION.keras')
 class Predict:
     def __init__(self, model, tokenizer):
         self.model = model