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2d6b76f
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Parent(s):
7caf3cc
Update app.py
Browse files
app.py
CHANGED
@@ -5,13 +5,41 @@ import streamlit.components.v1 as components
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.model_selection import train_test_split
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import pandas as pd
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# Separate target and feature column in X and y variable
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df = pd.read_csv('stress.csv')
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# X will be the features
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X = np.array(df["text"])
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# y will be the target variable
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y = np.array(df["label"])
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cv = CountVectorizer()
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# Load the pickled model
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@@ -40,7 +68,7 @@ def app_design():
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features = text # add features according to notebook
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# Make a prediction when the user clicks the "Predict" button
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if st.button('Predict
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predicted_value = model_prediction(features)
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if predicted_value == "['Stress']":
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st.success("Your message contains Stress")
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.model_selection import train_test_split
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import pandas as pd
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import nltk
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import re
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nltk.download('stopwords')
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stemmer = nltk.SnowballStemmer("english")
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from nltk.corpus import stopwords
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import string
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stopword=set(stopwords.words('english'))
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# Separate target and feature column in X and y variable
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df = pd.read_csv('stress.csv')
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# X will be the features
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def clean(text):
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text = str(text).lower()
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text = re.sub('\[.*?\]', '', text)
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text = re.sub('https?://\S+|www\.\S+', '', text)
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text = re.sub('<.*?>+', '', text)
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text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
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text = re.sub('\n', '', text)
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text = re.sub('\w*\d\w*', '', text)
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text = [word for word in text.split(' ') if word not in stopword]
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text=" ".join(text)
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text = [stemmer.stem(word) for word in text.split(' ')]
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text=" ".join(text)
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return text
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df["text"] = df["text"].apply(clean)
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X = np.array(df["text"])
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# y will be the target variable
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y = np.array(df["label"])
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df["text"] = df["text"].apply(clean)
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cv = CountVectorizer()
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# Load the pickled model
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features = text # add features according to notebook
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# Make a prediction when the user clicks the "Predict" button
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if st.button('Predict Stress'):
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predicted_value = model_prediction(features)
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if predicted_value == "['Stress']":
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st.success("Your message contains Stress")
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