Upload 3 files

app.py

@@ -1,20 +1,27 @@
 import streamlit as st
-import numpy as np
 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")
-def transform(password):
-    character=[]
-    for i in password:
-        character.append(i)
-    return character
-vect=CountVectorizer(tokenizer=transform)
-input=st.text_input("Your Password",type="password")
-if input is not None:
-   data=vect.fit_transform([input]).toarray()
-if st.button("Predict"):
-    prediction=model.predict(data)
-    class_names=["Weak","Medium","Strong"]
-    prediction=class_names(np.argmax(prediction))
-    st.write(prediction)
+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)

model.h5

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:653e6066760ee1ddee2036a034a06c8d876b35b132a52eb6810a3b2e9e3f84da
-size 463360
+oid sha256:732d0fa8473925ef2a813886b6570cc0e9fc8b0d03fa1319573150c09a86a83a
+size 17102424

requirements.txt

@@ -1,4 +1,6 @@
-scikit-learn
-numpy
 streamlit
-tensorflow
+scikit-learn
+textblob
+tensorflow
+nltk
+numpy