Merging reports and code

README.md

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-# CourseProject
+# CS410 Spiderman
 
-Please fork this repository and paste the github link of your fork on Microsoft CMT. Detailed instructions are on Coursera under Week 1: Course Project Overview/Week 9 Activities.
+Please click here for the video presentation:
+ Part I: https://youtu.be/0utoLsXVr9Y
+ Part II: https://youtu.be/o_V7v0G5xS8

dockerfile

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+FROM python:3.8
+#Labels as key value pair
+LABEL Maintainer="[email protected]"
+
+
+# Any working directory can be chosen as per choice like '/' or '/home' etc
+# i have chosen /usr/app/src
+WORKDIR /Users/sumababu1/sarcasm_config
+COPY requirements.txt requirements.txt
+RUN pip3 install --upgrade pip
+RUN pip3 install tensorflow
+RUN pip3 install -r requirements.txt 
+RUN pip3 install numpy --upgrade
+RUN pip3 install requests
+COPY . .
+
+
+#to COPY the remote file at working directory in container
+COPY sarcasm_final.py ./
+# Now the structure looks like this '/usr/app/src/test.py'
+
+
+#CMD instruction should be used to run the software
+#contained by your image, along with any arguments.
+
+CMD [ "python", "./sarcasm_final.py"]
+

requirements.txt

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+tensorflow
+requests
+numpy

sarcasm_final.py

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+import json
+import tensorflow as tf
+import numpy as np
+import urllib
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import tensorflow as tf
+import numpy as np
+import requests
+
+
+def solution_model():
+    url = 'http://storage.googleapis.com/download.tensorflow.org/data/sarcasm.json'
+    #datastore = urllib.request.urlretrieve(url, 'sarcasm.json')
+    datastore = requests.get('http://storage.googleapis.com/download.tensorflow.org/data/sarcasm.json')
+    # DO NOT CHANGE THIS CODE OR THE TESTS MAY NOT WORK
+    vocab_size = 1000
+    embedding_dim = 16
+    max_length = 120
+    trunc_type='post'
+    padding_type='post'
+    oov_tok = "<OOV>"
+    training_size = 20000
+
+    sentences = []
+    labels = []
+    # YOUR CODE HERE
+    for item in datastore.json():
+        sentences.append(item['headline'])
+        labels.append(item['is_sarcastic'])
+
+    training_sentences = sentences[0:training_size]
+    testing_sentences = sentences[training_size:]
+    training_labels = labels[0:training_size]
+    testing_labels = labels[training_size:]
+
+    tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)
+    tokenizer.fit_on_texts(training_sentences)
+
+    word_index = tokenizer.word_index
+
+    training_sequences = tokenizer.texts_to_sequences(training_sentences)
+    training_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
+
+    testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
+    testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
+    training_padded = np.array(training_padded)
+    training_labels = np.array(training_labels)
+    testing_padded = np.array(testing_padded)
+    testing_labels = np.array(testing_labels)
+    model = tf.keras.Sequential([
+        tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
+        tf.keras.layers.GlobalAveragePooling1D(),
+        tf.keras.layers.Dense(24, activation='relu'),
+    # YOUR CODE HERE. KEEP THIS OUTPUT LAYER INTACT OR TESTS MAY FAIL
+        tf.keras.layers.Dense(1, activation='sigmoid')
+    ])
+    num_epochs = 10
+    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+    model.fit(training_padded, training_labels, epochs=num_epochs,
+                        validation_data=(testing_padded, testing_labels), verbose=2)
+
+    reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
+
+    def decode_sentence(text):
+        return ' '.join([reverse_word_index.get(i, '?') for i in text])
+
+    #print(decode_sentence(training_padded[0]))
+    #print(training_sentences[2])
+    #print(labels[2])
+    e = model.layers[0]
+    weights = e.get_weights()[0]
+    #print(weights.shape)  # shape: (vocab_size, embedding_dim)
+    return model
+
+
+# Note that you'll need to save your model as a .h5 like this.
+# When you press the Submit and Test button, your saved .h5 model will
+# be sent to the testing infrastructure for scoring
+# and the score will be returned to you.
+if __name__ == '__main__':
+    vocab_size = 1000
+    embedding_dim = 16
+    max_length = 120
+    trunc_type = 'post'
+    padding_type = 'post'
+    oov_tok = "<OOV>"
+    training_size = 20000
+
+    model = solution_model()
+    #model.summary()
+    tokenizer = Tokenizer(oov_token="<OOV>")
+    #sentence = "facebook reportedly working on healthcare features and apps"
+    #sentence = "sentence = I really think this is amazing. honest."
+
+
+    sentence = ["obama visits arlington national cemetery to honor veterans",
+            "why writers must plan to be surprise",
+            "gillian jacobs on what it's like to kiss adam brody",
+            "rescuers heroically help beached garbage back into ocean",
+            "christian bale visits sikh temple victims",
+            "brita-unveils-new-in-throat-water-filters"]
+    tokenizer.fit_on_texts(sentence)
+    sequences = tokenizer.texts_to_sequences(sentence)
+
+
+    #sequence = tokenizer.texts_to_sequences([sentence])
+    #print(sequence)
+
+
+    #sequences = tokenizer.texts_to_sequences(sentence)
+    padded = pad_sequences(sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
+    #print(padded)
+    print(model.predict(padded))
+    #model.save("sarcasm.h5")