Update app.py

app.py

@@ -4,9 +4,6 @@ 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",
@@ -16,39 +13,17 @@ 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)
 
 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)
+    result = "IDK"
     st.write(f"TONE: {result}")