Update app.py

app.py

@@ -1,46 +1,46 @@
-import streamlit as st
-import pickle
-import re
-from sklearn.feature_extraction.text import  CountVectorizer
-
-with open('count_vectorizer.pkl','rb')as vectorizer_file:
-    count_vectorizer = pickle.load(vectorizer_file)
-
-with open('nb_classifier.pkl','rb')as classifier_file:
-    nb_classifier = pickle.load(classifier_file)
-
-def process_text(text):
-    text = text.lower()
-    text = re.sub(r'http\S+', '', text)
-    text = re.sub(r'@[a-zA-Z0-9_]+', '', text)
-    text = re.sub(r'#', '', text)
-    text = re.sub(r'[^a-zA-Z\s]', '', text)
-    return text
-
-sentiment_mapping = {
-        "Negative" : "Negative 😔",
-        "Positive" : "Positive 😊",
-        "Neutral" : "Neutral 🙄",
-        "Irrelevant" : "Irrelevant 🤷‍♂️"
-    }
-
-def main():
-    col1 , col2 , col3 ,col4 = st.columns([1,1,3,1])
-    with col3:
-        st.image("./image/pngwing.com (1).png" , width=100)
-    st.title("Twitter Sentiment Classifier")
-    st.write("Enter twitter tweet below :")
-    input_text = st.text_area("Input Text :","")
-    if st.button("Predict"):
-        cleaned_text = process_text(input_text)
-        vectorizer_text = count_vectorizer.transform([cleaned_text])
-        sentiment_prediction = nb_classifier.predict(vectorizer_text)[0]
-        
-        predicted_sentiment =  sentiment_mapping.get(sentiment_prediction , "Unknown Sentiment")
-
-        st.write("Predicted Sentimen :")
-        st.title(predicted_sentiment)
-
-
-if __name__  == "__main__":
+import streamlit as st
+import pickle
+import re
+from sklearn.feature_extraction.text import  CountVectorizer
+
+with open('count_vectorizer.pkl','rb')as vectorizer_file:
+    count_vectorizer = pickle.load(vectorizer_file)
+
+with open('nb_classifier.pkl','rb')as classifier_file:
+    nb_classifier = pickle.load(classifier_file)
+
+def process_text(text):
+    text = text.lower()
+    text = re.sub(r'http\S+', '', text)
+    text = re.sub(r'@[a-zA-Z0-9_]+', '', text)
+    text = re.sub(r'#', '', text)
+    text = re.sub(r'[^a-zA-Z\s]', '', text)
+    return text
+
+sentiment_mapping = {
+        "Negative" : "Negative 😔",
+        "Positive" : "Positive 😊",
+        "Neutral" : "Neutral 🙄",
+        "Irrelevant" : "Irrelevant 🤷‍♂️"
+    }
+
+def main():
+    col1 , col2 , col3 ,col4 = st.columns([1,1,3,1])
+    with col3:
+        st.image("./pngwing.com (1).png" , width=100)
+    st.title("Twitter Sentiment Classifier")
+    st.write("Enter twitter tweet below :")
+    input_text = st.text_area("Input Text :","")
+    if st.button("Predict"):
+        cleaned_text = process_text(input_text)
+        vectorizer_text = count_vectorizer.transform([cleaned_text])
+        sentiment_prediction = nb_classifier.predict(vectorizer_text)[0]
+        
+        predicted_sentiment =  sentiment_mapping.get(sentiment_prediction , "Unknown Sentiment")
+
+        st.write("Predicted Sentimen :")
+        st.title(predicted_sentiment)
+
+
+if __name__  == "__main__":
     main()