Initial commit

Author: pycoder2000

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

Procfile

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+web: gunicorn app:app

README.md

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+# Sentiment Analysis
+A simple sentiment analysis application made using following packages:
+- Tensorflow Keras
+- Natural Language Toolkit (NLTK)
+- TfidfVectorizer
+
+
+The model was trained using tweets from [Sentiment140 dataset with 1.6 million tweets](http://www.kaggle.com/kazanova/sentiment140).
+
+The app has has been deployed using Flask and Heroku. The app can be found [here](http://sentiment-analysis-mp.herokuapp.com/).

app.py

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+import numpy as np
+from flask import Flask, request, jsonify, render_template
+import pickle
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+import tensorflow as tf
+import re
+from nltk.corpus import stopwords
+from nltk.stem import SnowballStemmer
+
+app = Flask(__name__)
+vect = pickle.load(open("vectorizer.pickle",'rb'))
+model = tf.keras.models.load_model('model.h5')
+
+def preprocess(text, stem=False):
+  text = re.sub("@\S+|https?:\S+|http?:\S|[^A-Za-z0-9]+", ' ', str(text).lower()).strip()
+  stop_words = stopwords.words('english')
+  stemmer = SnowballStemmer('english')
+  tokens = []
+  for token in text.split():
+    if token not in stop_words:
+      if stem:
+        tokens.append(stemmer.stem(token))
+      else:
+        tokens.append(token)
+  return " ".join(tokens)
+
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/predict',methods=['POST'])
+def predict():
+    inp = [x for x in request.form.values()]
+    inp = preprocess(inp)
+    inp = vect.transform([inp]).toarray().reshape(1,1,2500)
+    output = model.predict(inp)[0]
+
+    return render_template('index.html', prediction_text='{}'.format(output[0]))
+
+if __name__ == "__main__":
+    app.run(debug=True)

model.h5

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+stopwords

nltk.txt

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+Flask==1.1.2
+numpy==1.18.5
+nltk==3.5
+tensorflow==2.3.1
+scikit-learn==0.23.2
+gunicorn==20.0.4