Sarcasm-Detection-Project (1).ipynb

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Non Sarcastic\n","1  ‘You Did The Best You Could,’ Says Iron Man Ac...      Sarcastic\n","2  New Emails Reveal Warm Relationship Between Ka...  Non Sarcastic\n","3  Donald Trump Jr. Gets Slammed Over Racist Birt...  Non Sarcastic\n","4      God Urges Rick Perry Not To Run For President      Sarcastic"],"text/html":["\n","  <div id=\"df-528734dc-47bc-4883-8c79-24278dffd276\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>headlines</th>\n","      <th>target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>CNN Triumphs (At Least in Most Demographic Cat...</td>\n","      <td>Non Sarcastic</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>‘You Did The Best You Could,’ Says Iron Man Ac...</td>\n","      <td>Sarcastic</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>New Emails Reveal Warm Relationship Between Ka...</td>\n","      <td>Non Sarcastic</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Donald Trump Jr. Gets Slammed Over Racist Birt...</td>\n","      <td>Non Sarcastic</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>God Urges Rick Perry Not To Run For President</td>\n","      <td>Sarcastic</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-528734dc-47bc-4883-8c79-24278dffd276')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-528734dc-47bc-4883-8c79-24278dffd276 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Sarcastic\n","12503  Report: North Dakota Leads Nation In Parking A...      Sarcastic\n","12504  The Election Night Drinking Game: HyperVocal E...  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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Non-sarcastic Comments"],"metadata":{"id":"qmbHImEXGxwG"}},{"cell_type":"code","source":["df[df['target']=='Non Sarcastic']['headlines'].iloc[29]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"Q2rl0mSmEUOa","executionInfo":{"status":"ok","timestamp":1715002483048,"user_tz":-330,"elapsed":18,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"a589ee78-25ec-432a-d784-4ee1a79e5efe"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"Sen. Kamala Harris' Guide To Protesting The Health Care Bill\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["df[df['target']=='Non Sarcastic']['headlines'].iloc[4]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"2EY3DsibHIkU","executionInfo":{"status":"ok","timestamp":1715002483049,"user_tz":-330,"elapsed":16,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"b22d6bc6-3a12-4f9b-a105-2f82f7d42557"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"CNN Anchor Calls Obama Protester 'Rude' And 'Crazy'\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["df[df['target']=='Non Sarcastic']['headlines'].iloc[30]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"F0Yi8VjTHNgL","executionInfo":{"status":"ok","timestamp":1715002483630,"user_tz":-330,"elapsed":593,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"6b3596c5-d828-4d02-a44b-2df37acb9d3c"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"Republic Flight Makes Emergency Landing In NY After Passenger's 'Erratic Behavior'\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["df[df['target']=='Non Sarcastic']['headlines'].iloc[3]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"rs2ZSyAhHRnw","executionInfo":{"status":"ok","timestamp":1715002483632,"user_tz":-330,"elapsed":51,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"d6e4d4c9-43bd-4fd5-fe90-49f77b956751"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Global Aid Pours into Haiti'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","source":["Scarcastic comments"],"metadata":{"id":"XFnA7yYXHY1B"}},{"cell_type":"code","source":["df[df['target']=='Sarcastic']['headlines'].iloc[15]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"CAiq2Q5RHVyO","executionInfo":{"status":"ok","timestamp":1715002483633,"user_tz":-330,"elapsed":48,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"1ce3db7c-7a4a-428f-d51a-5fde30c19b74"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Mayor Of Phoenix Apologizes For Naming Berlin Germany Of 1941 As Sister City'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["df[df['target']=='Sarcastic']['headlines'].iloc[17]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"V1_LfvR1Hf5Z","executionInfo":{"status":"ok","timestamp":1715002483633,"user_tz":-330,"elapsed":46,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"ce8d80c9-b3c7-4503-bfbd-f1cad5d99627"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Report: Average American Feels Comfortable In Own Skin For Only 6% Of Day'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["df[df['target']=='Sarcastic']['headlines'].iloc[2]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"ze63r1tPHjiw","executionInfo":{"status":"ok","timestamp":1715002483633,"user_tz":-330,"elapsed":43,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"dab70cb2-5569-4ead-d675-aaaa496892dd"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Federal Prisons Reinstitute Executions By Lethal Inflation'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["df[df['target']=='Sarcastic']['headlines'].iloc[3]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"nL3fhewZHl94","executionInfo":{"status":"ok","timestamp":1715002483634,"user_tz":-330,"elapsed":43,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"fce7f93b-cecf-4330-bea2-f0adb0a0d88c"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Soldier Back Home From Serving At Mexico Border Still Having Nightmares About Being Used As Political Prop'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["df[df['target']=='Non Sarcastic']['headlines'].iloc[43]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"dKdPCbJPHpgO","executionInfo":{"status":"ok","timestamp":1715002483634,"user_tz":-330,"elapsed":38,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"6160dd80-b7dd-4ab0-b971-6a3409303514"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Who Needs Journalism?'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","source":["Preprocessing"],"metadata":{"id":"aO25IJSvJxR7"}},{"cell_type":"markdown","source":["convert everythong to lowercase"],"metadata":{"id":"Merd067SJ01t"}},{"cell_type":"code","source":["df['headlines']=df['headlines'].str.lower()"],"metadata":{"id":"sdY4FUhBHu9J","executionInfo":{"status":"ok","timestamp":1715002483634,"user_tz":-330,"elapsed":34,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":["Remove puncutaions and stop words"],"metadata":{"id":"QEBr_Z7YKKgk"}},{"cell_type":"code","source":["nltk.download('stopwords')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mVGDQDCzJ_Qw","executionInfo":{"status":"ok","timestamp":1715002483635,"user_tz":-330,"elapsed":33,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"6adc5046-158b-4b8e-8129-affd8bc53439"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data]   Unzipping corpora/stopwords.zip.\n"]},{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["stop_words=nltk.corpus.stopwords.words('english')\n","punct=string.punctuation"],"metadata":{"id":"OeB4BK5DKQhE","executionInfo":{"status":"ok","timestamp":1715002483635,"user_tz":-330,"elapsed":27,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["df['headlines']=df['headlines'].replace(stop_words,'')\n","df['headlines']=df['headlines'].replace(punct,'')"],"metadata":{"id":"uALyAL7yKjoK","executionInfo":{"status":"ok","timestamp":1715002484075,"user_tz":-330,"elapsed":466,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["def to_binary(x):\n","  if x =='Sarcastic':\n","    return 1\n","  else:\n","    return 0"],"metadata":{"id":"Azs0qqSjLIQ5","executionInfo":{"status":"ok","timestamp":1715002484075,"user_tz":-330,"elapsed":15,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["df['target']=df['target'].apply(to_binary)"],"metadata":{"id":"sRQ6xT-CLnzg","executionInfo":{"status":"ok","timestamp":1715002484076,"user_tz":-330,"elapsed":14,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["df.columns"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"v31rH0N0MTfv","executionInfo":{"status":"ok","timestamp":1715002484076,"user_tz":-330,"elapsed":12,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"bbea8f38-b598-4192-d052-1831e122b46a"},"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['headlines', 'target'], dtype='object')"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["input=df['headlines']\n","output=df['target']"],"metadata":{"id":"kzzORU4RMWwT","executionInfo":{"status":"ok","timestamp":1715002484076,"user_tz":-330,"elapsed":5,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["input_train, input_test, output_train, output_test = train_test_split(input, output, test_size=0.3, random_state=42)"],"metadata":{"id":"7chVTTdsMeBu","executionInfo":{"status":"ok","timestamp":1715002484662,"user_tz":-330,"elapsed":18,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["num_words = 10000"],"metadata":{"id":"eoo0Tu7iM2GT","executionInfo":{"status":"ok","timestamp":1715002484663,"user_tz":-330,"elapsed":17,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":24,"outputs":[]},{"cell_type":"code","source":["tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words = num_words,\n","                                                  filters = '!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~',\n","                                                  oov_token = '<oov>')"],"metadata":{"id":"CmEHE8A9NrCU","executionInfo":{"status":"ok","timestamp":1715002484664,"user_tz":-330,"elapsed":8,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":["tokenizer.fit_on_texts(input_train)"],"metadata":{"id":"w8SejBO7PC9D","executionInfo":{"status":"ok","timestamp":1715002485506,"user_tz":-330,"elapsed":848,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":26,"outputs":[]},{"cell_type":"code","source":["word_index=tokenizer.word_index"],"metadata":{"id":"mU1_CJk3QGWX","executionInfo":{"status":"ok","timestamp":1715002485507,"user_tz":-330,"elapsed":13,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":27,"outputs":[]},{"cell_type":"code","source":["reverse_word_index = dict([ (v,k) for (k,v) in word_index.items ()])"],"metadata":{"id":"9sw0s6Z6PUIB","executionInfo":{"status":"ok","timestamp":1715002485507,"user_tz":-330,"elapsed":12,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":28,"outputs":[]},{"cell_type":"code","source":["vocab_size = len(word_index)"],"metadata":{"id":"573vYKgoQKgv","executionInfo":{"status":"ok","timestamp":1715002485508,"user_tz":-330,"elapsed":13,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":29,"outputs":[]},{"cell_type":"code","source":["vocab_size"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IFKNZ0qcQsU-","executionInfo":{"status":"ok","timestamp":1715002485509,"user_tz":-330,"elapsed":13,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"733a0fb3-a701-41fa-99f9-2d38dae74869"},"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"text/plain":["16528"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","source":["input_train = tokenizer.texts_to_sequences(input_train)\n","input_test = tokenizer.texts_to_sequences(input_test)"],"metadata":{"id":"urg56YwJQude","executionInfo":{"status":"ok","timestamp":1715002486056,"user_tz":-330,"elapsed":558,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":31,"outputs":[]},{"cell_type":"code","source":["max_len = max([len(s) for s in input_train])\n","max_len"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TsIlZbEXREh1","executionInfo":{"status":"ok","timestamp":1715002486057,"user_tz":-330,"elapsed":8,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"6c419893-ab4e-4d1f-807b-c7dbc8adf915"},"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["39"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","source":["input_train = tf.keras.utils.pad_sequences(input_train, padding = 'pre', maxlen=max_len)\n","input_test = tf.keras.utils.pad_sequences(input_test, padding = 'pre', maxlen=max_len)"],"metadata":{"id":"V2UITVvxRaJP","executionInfo":{"status":"ok","timestamp":1715002486607,"user_tz":-330,"elapsed":554,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":33,"outputs":[]},{"cell_type":"markdown","source":["Modeling"],"metadata":{"id":"m_v2OPdES9cu"}},{"cell_type":"code","source":["\n","\n"],"metadata":{"id":"TewLryhqS6Y3","executionInfo":{"status":"ok","timestamp":1715002486608,"user_tz":-330,"elapsed":5,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":33,"outputs":[]},{"cell_type":"code","source":["\n","embd_dim = 128  # Example value for the embedding dimension\n","rec_nns = 64  # Example value for the number of units in the LSTM layer\n","rec_act = 'tanh'  # Example activation function for the LSTM layer\n","\n","model = tf.keras.models.Sequential([\n","    tf.keras.layers.Embedding(num_words, embd_dim, input_length=max_len),\n","    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(rec_nns, activation=rec_act)),\n","    tf.keras.layers.Dropout(0.3),\n","    tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)\n","])\n","\n","model.compile(\n","    loss=tf.keras.losses.BinaryCrossentropy(),\n","    optimizer=tf.keras.optimizers.Adam(),\n","    metrics=['accuracy']\n",")\n","\n","model.summary()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2ezxtU0iSxqb","executionInfo":{"status":"ok","timestamp":1715002490722,"user_tz":-330,"elapsed":4118,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"de7c4dde-6a8d-4179-f604-6e3fd1f5e4ac"},"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding (Embedding)       (None, 39, 128)           1280000   \n","                                                                 \n"," bidirectional (Bidirection  (None, 128)               98816     \n"," al)                                                             \n","                                                                 \n"," dropout (Dropout)           (None, 128)               0         \n","                                                                 \n"," dense (Dense)               (None, 1)                 129       \n","                                                                 \n","=================================================================\n","Total params: 1378945 (5.26 MB)\n","Trainable params: 1378945 (5.26 MB)\n","Non-trainable params: 0 (0.00 Byte)\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["history = model.fit(input_train, output_train,\n","                    epochs = 5,\n","                    validation_data = (input_test, output_test))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TBWV_B7BVhMn","executionInfo":{"status":"ok","timestamp":1715002647626,"user_tz":-330,"elapsed":156925,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"33ad520b-cc2b-435f-b148-4d687c47c53b"},"execution_count":35,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","274/274 [==============================] - 40s 90ms/step - loss: 0.3485 - accuracy: 0.8377 - val_loss: 0.2475 - val_accuracy: 0.8993\n","Epoch 2/5\n","274/274 [==============================] - 24s 89ms/step - loss: 0.1075 - accuracy: 0.9622 - val_loss: 0.2507 - val_accuracy: 0.8993\n","Epoch 3/5\n","274/274 [==============================] - 25s 93ms/step - loss: 0.0386 - accuracy: 0.9888 - val_loss: 0.3187 - val_accuracy: 0.8985\n","Epoch 4/5\n","274/274 [==============================] - 26s 96ms/step - loss: 0.0162 - accuracy: 0.9950 - val_loss: 0.4455 - val_accuracy: 0.8915\n","Epoch 5/5\n","274/274 [==============================] - 23s 83ms/step - loss: 0.0151 - accuracy: 0.9960 - val_loss: 0.4095 - val_accuracy: 0.8937\n"]}]},{"cell_type":"code","source":["pd.DataFrame(history.history).plot()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"OxQlb_X5WJe0","executionInfo":{"status":"ok","timestamp":1715002647627,"user_tz":-330,"elapsed":22,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"95138b64-7ea7-41f6-dfb5-291e2088e03b"},"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":36},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["def preprocess_sentence(sentence):\n","    # Convert to lowercase\n","    sentence = sentence.lower()\n","\n","    # Remove punctuation and stop words\n","    sentence = ''.join([char for char in sentence if char not in string.punctuation])\n","    sentence = ' '.join([word for word in sentence.split() if word not in stop_words])\n","\n","    return sentence\n","\n","def predict_sarcasm(sentence):\n","    # Preprocess the input sentence\n","    preprocessed_sentence = preprocess_sentence(sentence)\n","\n","    # Tokenize the preprocessed sentence\n","    tokenized_sentence = tokenizer.texts_to_sequences([preprocessed_sentence])\n","\n","    # Pad the tokenized sentence\n","    padded_sentence = tf.keras.preprocessing.sequence.pad_sequences(tokenized_sentence,\n","                                                                    padding='pre',\n","                                                                    maxlen=max_len)\n","\n","    # Make prediction\n","    prediction = model.predict(padded_sentence)[0][0]\n","\n","    # Determine the sarcasm label based on prediction threshold (e.g., 0.5)\n","    sarcasm_label = 'Sarcastic' if prediction >= 0.5 else 'Non-Sarcastic'\n","\n","    return sarcasm_label, prediction\n","\n","# Example usage\n","input_sentence = \"He ate like there was no tomorrow\"\n","sarcasm_label, sarcasm_probability = predict_sarcasm(input_sentence)\n","print(f\"Sarcasm Label: {sarcasm_label}\")\n","print(f\"Sarcasm Probability: {sarcasm_probability}\")\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S2nUEQHKWbos","executionInfo":{"status":"ok","timestamp":1715002960999,"user_tz":-330,"elapsed":1122,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"outputId":"ee188938-5b05-4735-ef44-cd8f92db7f66"},"execution_count":41,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 196ms/step\n","Sarcasm Label: Sarcastic\n","Sarcasm Probability: 0.9661017060279846\n"]}]},{"cell_type":"code","source":["model.save(\"sd.keras\")"],"metadata":{"id":"KYCWms7hmd5m","executionInfo":{"status":"ok","timestamp":1715002648745,"user_tz":-330,"elapsed":4,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":38,"outputs":[]},{"cell_type":"code","source":["!pip install gradio\n","\n","import ipywidgets as widgets\n","from IPython.display import display\n","import gradio as gr\n","\n","# def predict(text):\n","#     \"\"\"\n","#     This function predicts the toxicity of the comment passed.\n","#     \"\"\"\n","#     predictions = model.predict([text])\n","#     result = str({column_name: prediction for column_name, prediction in zip(df.columns[2:], np.squeeze(predictions))})\n","#     output_string = f\"Prediction for '{text}' ===> Results: {result}\"\n","#     return output_string\n","\n","# interact(predict, text=Textarea(value='', placeholder='Type your text here', description='Text:'))\n","\n","input_text = gr.Textbox(lines=2, label=\"Enter your sentence...\")\n","output_text = gr.Textbox(label=\"Sarcasm Prediction\")\n","\n","def predict(input_sentence):\n","    sarcasm_label, sarcasm_probability = predict_sarcasm(input_sentence)\n","    return f\"Sarcasm Label: {sarcasm_label}, Sarcasm Probability: {sarcasm_probability:.2f}\"\n","\n","iface = gr.Interface(fn=predict, inputs=input_text, outputs=output_text, title=\"Sarcasm Detection\")\n","iface.launch()"],"metadata":{"id":"fEpcrRlAmi5K","executionInfo":{"status":"ok","timestamp":1715002936046,"user_tz":-330,"elapsed":11028,"user":{"displayName":"Nikita","userId":"05173413370276352760"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"fbb4ef69-d3a7-4d43-bf77-a5eadd209c73"},"execution_count":40,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: gradio in /usr/local/lib/python3.10/dist-packages (4.29.0)\n","Requirement already satisfied: aiofiles<24.0,>=22.0 in 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Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n","\n","Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n","Running on public URL: https://bf279e8e493cff1518.gradio.live\n","\n","This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<div><iframe src=\"https://bf279e8e493cff1518.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":[]},"metadata":{},"execution_count":40}]},{"cell_type":"code","source":[],"metadata":{"id":"Pe9YXwy_HNm8","executionInfo":{"status":"ok","timestamp":1715002678057,"user_tz":-330,"elapsed":8,"user":{"displayName":"Nikita","userId":"05173413370276352760"}}},"execution_count":39,"outputs":[]}]}