Upload 3 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+combined_emotion.csv filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,34 @@
+import streamlit as stl
+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import accuracy_score
+stl.write('this is sentiment analysis')
+df = pd.read_csv(r'combined_emotion.csv')
+model1 = Pipeline([("Feature_Engineer",CountVectorizer(binary= True,stop_words = 'english')),
+                  ("Algorithm", MultinomialNB())])
+X = df['sentence']
+y = df['emotion']
+
+x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=23)
+model1.fit(x_train,y_train)
+y_pred = model1.predict(x_test)
+if stl.button('click for accuracy'):
+    stl.write(accuracy_score(y_test,y_pred))
+str = stl.text_input('enter text')
+if stl.button('click to check'):
+      if model1.predict([str]) == 'sad':
+           stl.write('sad ☹️')
+      elif model1.predict([str]) == 'angry':
+           stl.write('anger 😡')
+      elif model1.predict([str]) == 'fear':
+           stl.write('fear 😨')
+      elif model1.predict([str]) == 'joy':
+           stl.write('joy 😁')
+      elif model1.predict([str]) == 'love':
+           stl.write('😍')
+      else :
+           stl.write('surprise 😲')

combined_emotion.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:704ba3b3a7d75180205d91449196048c2affc54e4a894988f88bdb8fd34f7c1d
+size 43819946

requirements.txt

@@ -0,0 +1,4 @@
+streamlit
+pandas
+numpy
+scikit-learn