Update app.py

app.py

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+import pandas as pd
+import numpy as np
+from tensorflow.keras import layers
+from tensorflow.keras import Input
+from tensorflow.keras.models import Model
+from tensorflow.keras.preprocessing import sequence
+from tensorflow.keras.preprocessing.text import Tokenizer
+import matplotlib.pyplot as plt
+from tensorflow.keras.callbacks import EarlyStopping
+df = pd.read_csv("train.csv")
+embeddings_index = {}
+f = open('glove.6B.100d.txt',encoding="utf")
+for line in f:
+    values = line.split()
+    word = values[0]
+    coefs = np.asarray(values[1:], dtype='float32')
+    embeddings_index[word] = coefs
+f.close()
+print('Found %s word vectors.' % len(embeddings_index))
+data = df.text
+labels = df.target
+x_train = data[0:6100]
+x_test = data[6100:]
+y_train = labels[0:6100] 
+y_test = labels[6100:]
+tokenizer = Tokenizer()
+tokenizer.fit_on_texts(x_train.values)
+sequences = tokenizer.texts_to_sequences(x_train.values)
+sequences = sequence.pad_sequences(sequences, maxlen=200)
+vocab_size = len(tokenizer.word_index)+1
+embedding_dim = 100
+max_words=1513
+
+embedding_matrix = np.zeros((vocab_size, embedding_dim))
+for word, i in tokenizer.word_index.items():
+    if i < max_words:
+        embedding_vector = embeddings_index.get(word)
+        if embedding_vector is not None:
+            embedding_matrix[i] = embedding_vector
+input_layer = Input(shape=(None,), dtype='int32', name='tweet_input')
+x = layers.Embedding(vocab_size, 100, input_length=200)(input_layer)
+x = layers.LSTM(32,
+dropout=0.1,
+recurrent_dropout=0.5,
+return_sequences=True)(x)
+x = layers.LSTM(32,
+dropout=0.1,
+recurrent_dropout=0.5,
+return_sequences=False)(x)
+x = layers.Dense(100, activation='relu')(x)
+output = layers.Dense(1, activation='sigmoid')(x)
+model = Model(input_layer,output)
+model.layers[1].set_weights([embedding_matrix])
+model.layers[1].trainable = False
+model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
+es = EarlyStopping(monitor='val_loss', mode='min')
+history = model.fit(sequences, y_train.values, epochs=20, validation_split=0.2, callbacks = [es])
+model.save("trained.h5")
+sequences = tokenizer.texts_to_sequences(x_test.values)
+sequences = sequence.pad_sequences(sequences, maxlen=200)
+x_test = sequences
+score = model.evaluate(x_test, y_test.values)
+test = pd.read_csv("test.csv")
+ids = test.id
+test = test.text
+sequences = tokenizer.texts_to_sequences(test)
+sequences = sequence.pad_sequences(sequences, maxlen=200)
+results = model.predict(sequences)
+results = results.round()
+results = results.squeeze()
+csv_df = pd.DataFrame({
+    "id": ids,
+    "target": results
+})
+csv_df.index = csv_df.id
+csv_df = csv_df["target"]
+csv_df = csv_df.astype(int)
+csv_df.to_csv("results.csv", header=True)
+def encoder(text):
+    text = tokenizer.texts_to_sequences([text])
+    text = sequence.pad_sequences(text, maxlen=200)
+    return text
+def predict(text):
+    encoded_text = encoder(text)
+#     print(encoded_text)
+    prediction = (model.predict(encoded_text))
+    print(prediction)
+    prediction = np.round(prediction)
+    if prediction==1:
+        return "Disaster"
+    return "Not a Disaster"
+import gradio as gr
+title="Relevance Classifier"
+description="<p style='text-align:center'>Classifies input text into Disaster-related or not disaster related."
+
+gr.Interface(fn=predict, inputs='text', outputs='text', title=title, description=description).launch()
+
+