Update app.py

app.py

@@ -1,13 +1,74 @@
 import streamlit as st
+import pandas as pd
+import numpy as ny
+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
 
-st.title("Sentiment Analysis with HuggingFace Spaces")
-st.write("Enter a sentence to analyze text's Emotion:")
+map_id = {
+  0: "sadness",
+  1: "anger",
+  2: "love",
+  3: "surprise",
+  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,
+         validation_data=(x_val, y_val))
+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 = ''
-    sentiment = ''
-    confidence = ''
-
-    st.write(f"Sentiment: {sentiment}")
-    st.write(f"Confidence: {confidence:.2f}")
+    result = predict.predict(user_input)
+    st.write(f"TONE: {result}")