Update app.py

app.py

@@ -1,27 +1,28 @@
-import streamlit as st
-from sklearn.feature_extraction.text import CountVectorizer
-from textblob import TextBlob
-from nltk.stem import PorterStemmer
-from tensorflow.keras.models import load_model
-import numpy as np
-import nltk
-
-model=load_model("model.h5")
-pr=PorterStemmer()
-def lemmafn(text):
-    words=TextBlob(text).words
-    return [pr.stem(word) for word in words]
-vect=CountVectorizer(ngram_range=(1,4),max_features=100000,analyzer=lemmafn)
-st.title("Predicting Emotion of Text")
-text=st.text_area("Your text")
-if text is not None:
-    text=text.lower()
-    text=text.replace("[^\w\s]","")
-    text=text.replace("\n","")
-    text=text.replace("\d+","")
-    text=vect.fit_transform([text])
-    if st.button("Predict"):
-       prediction=model.predict(text)
-       class_names=["Joy","Love","Anger","Sadness","Fear","Surprise"]
-       emotion=class_names[np.argmax(prediction)]
+import streamlit as st
+from sklearn.feature_extraction.text import CountVectorizer
+from textblob import TextBlob
+from nltk.stem import PorterStemmer
+from tensorflow.keras.models import load_model
+import numpy as np
+import nltk
+nltk.download("punkt")
+
+model=load_model("model.h5")
+pr=PorterStemmer()
+def lemmafn(text):
+    words=TextBlob(text).words
+    return [pr.stem(word) for word in words]
+vect=CountVectorizer(ngram_range=(1,4),max_features=100000,analyzer=lemmafn)
+st.title("Predicting Emotion of Text")
+text=st.text_area("Your text")
+if text is not None:
+    text=text.lower()
+    text=text.replace("[^\w\s]","")
+    text=text.replace("\n","")
+    text=text.replace("\d+","")
+    text=vect.fit_transform([text])
+    if st.button("Predict"):
+       prediction=model.predict(text)
+       class_names=["Joy","Love","Anger","Sadness","Fear","Surprise"]
+       emotion=class_names[np.argmax(prediction)]
        st.write(emotion)