Delete app.py

app.py

@@ -1,76 +0,0 @@
-import keras
-import pickle
-import tempfile
-import numpy as np
-import gradio as gr
-import tensorflow as tf
-from tensorflow.keras.layers import Layer
-from tensorflow.keras import backend as K
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-
-
-class Attention(Layer):
- 
- def __init__(self, return_sequences=True, **kwargs):
- self.return_sequences = return_sequences
- super(Attention, self).__init__(**kwargs)
-
- def build(self, input_shape):
- 
- self.W=self.add_weight(name="att_weight", shape=(input_shape[-1],1),
- initializer="normal")
- self.b=self.add_weight(name="att_bias", shape=(input_shape[1],1),
- initializer="zeros")
- 
- super(Attention,self).build(input_shape)
- 
- def call(self, x):
- 
- e = K.tanh(K.dot(x,self.W)+self.b)
- a = K.softmax(e, axis=1)
- output = x*a
- 
- if self.return_sequences:
- return output
- 
- return K.sum(output, axis=1)
- 
-
-
-def load_tokenizer(path):
- with open(path, 'rb') as f:
- tokenizer = pickle.load(f)
- return tokenizer
-
-
-def label_tweet(test_review):
- test_review = test_review.lower().strip()
- token_list = tokenizer.texts_to_sequences([test_review])[0]
- token_list = pad_sequences([token_list], maxlen=44, padding='post')
- predicted = model.predict(token_list, verbose=0)
- if predicted >= 0.5:
- return 1
- else: 
- return 0
-
-
-def analyze_text(comment):
- 
- result = label_tweet(comment)
- if result == 0: 
- text = "Negative"
- else:
- text = "Positive"
- return text
-
-
-# It can be used to reconstruct the model identically.
-model = keras.models.load_model("twitter_sentiment.keras",
- custom_objects={'Attention': Attention})
-
-# Load tokenizer
-tokenizer = load_tokenizer('tokenizer.pkl')
-
-interface = gr.Interface(fn=analyze_text, inputs=gr.inputs.Textbox(lines=2, placeholder='Enter a positive or negative tweet here...'),
- outputs='text',title='Twitter Sentimental Analysis', theme='darkhuggingface')
-interface.launch(inline=False)