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d11c05f
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Parent(s):
85c4ea5
Update app.py
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app.py
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#Import required libraries
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import pickle
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import gradio as gr
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import gradio.inputs
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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#Loading the tokenizer
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with open('tokenizer.pickle', 'rb') as f:
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tokenizer = pickle.load(f)
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def predict_sentiment(text):
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sentiment = ["I guess, I liked the movie, but I'm not sure it's my favorite."]
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sequence_test = tokenizer.texts_to_sequences([text])
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padded_test = pad_sequences(sequence_test, maxlen= 64)
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text=padded_test
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model = from_pretrained_keras("keras-io/bidirectional-lstm-imdb")
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X = [text for _ in range(len(model.input))]
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a=model.predict(X)
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return sentiment[np.around(a, decimals=0).argmax(axis=1)[0]]
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description = "Provide an opinion regarding a movie as input and this app will suggest what the underlying sentiment is. "
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inputs= gradio.inputs.Textbox( lines=1, placeholder=None, default="", label=None),
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outputs='text',
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title="Sentiment Analysis of Movie Reviews",
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description=description,
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theme="grass")
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iface.launch(enable_queue = True, inline=False, share = True)
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import gradio as gr
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import tensorflow as tf
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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import pickle
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from huggingface_hub import from_pretrained_keras
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model = from_pretrained_keras("keras-io/bidirectional-lstm-imdb")
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with open('tokenizer.pickle', 'rb') as file:
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tokenizer = pickle.load(file)
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def decide(text):
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tokenized_text = tokenizer.texts_to_sequences([text])
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padded_tokens = pad_sequences(tokenized_text, maxlen= 200)
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result = model.predict(padded_tokens, verbose=0)
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if result[:] < 0.5 :
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output = "negative"
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else:
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output = "positive"
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return output
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example_sentence_1 = "I hate the movie, they made no effort in making the movie. Waste of time!"
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example_sentence_2 = "Awesome movie! Loved the way in which the hero acted."
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examples = [[example_sentence_1], [example_sentence_2]]
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description = "Write out a movie review to know the sentiment."
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gr.Interface(decide, inputs= gr.inputs.Textbox( lines=1, placeholder=None, default="", label=None), outputs='text', examples=examples,
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title="Sentiment analysis of movie reviews",description=description, allow_flagging="auto",
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flagging_dir='flagging records').launch(inline=False, share = True)
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