Add files to huggingFace

app.py

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+import streamlit as st
+import pickle
+from nltk.stem.porter import PorterStemmer
+import numpy as np
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.preprocessing import LabelEncoder
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.model_selection import train_test_split
+
+data = (pickle.load(open("dataframe.pkl", "rb")))
+features_dict = pickle.load(open("features_dict.pkl", "rb"))
+
+le = LabelEncoder()
+cv = CountVectorizer(max_features=5000)
+clf=MultinomialNB()
+
+def vectorize(dataframe):
+    X = cv.fit_transform(dataframe.review).toarray()
+    data.sentiment=le.fit_transform(dataframe.sentiment)
+    y=data.iloc[:,-1].values
+    return X, y
+
+def stemming(text, stemmer=PorterStemmer()):
+    stem_word=[]
+    for i in text.split():
+        stem_word.append(stemmer.stem(i))
+    return stem_word
+
+#store the text in BoW(bag of words)
+def vectorBuild(val):
+    a=np.zeros(5000)
+    for i in range(len(val)):
+        if val[i] in features_dict:
+            a[features_dict[val[i]]] += 1
+    a = a.reshape(1, -1)
+    return a
+
+def modelFunction(X, y, a):
+    train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2, random_state=42, stratify=data.sentiment)
+    clf.fit(train_X, train_y)
+    pred = clf.predict(a)
+    return pred
+
+st.title("Sentiment Analysis Prediction")
+
+container = st.container()
+container.write("You need to press enter everytime, empty textbox will show \'Positve Sentiment\'")
+container.write()
+selected_text = container.text_input("Enter the text that you want to test")
+container.write("Processing...")
+
+# selected_text = input("Enter the text that you want to test: ")
+a = stemming(selected_text)
+a = vectorBuild(a)
+X, y = vectorize(data)
+model = modelFunction(X, y, a)
+
+if model[0] == 1:
+    container.write("Positive Sentiment")
+    # print("Positive")
+elif model[0] == 0:
+    container.write("Negative Sentiment")
+    # print("Negative")

dataframe.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3d911bc8b9e89e32d21ad2067d1a78e811072505f7785ff9382971939dba1ae4
+size 2416637

features_dict.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6df728b35640b23ffee15c2e4de10418386df348ecd0faba878dc225d5eea21e
+size 55255