app.py

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)