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import tensorflow as tf
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import pickle
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import gradio as gr
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import numpy as np
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import json
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with open('tokenizer.json') as file:
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tokenizer_data = file.read()
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tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(tokenizer_data)
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def Infernce_Pipe(text,max_length = 100):
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model = tf.keras.models.load_model("LSTM_senti.h5")
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sequences = tokenizer.texts_to_sequences([text])
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padded = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=max_length, padding='post', truncating='post')
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pred = model.predict(padded)
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predicted_index = np.argmax(pred)
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labels = ['Negative', 'Neutral', 'Positive']
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predicted_label = labels[predicted_index]
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return predicted_label
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interface = gr.Interface(
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fn=Infernce_Pipe,
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inputs=gr.Textbox(placeholder="Enter text here..."),
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outputs=gr.Text(label="Prediction"),
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title="Sentiment Analysis on Customer Review",
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description="Enter a review to get its sentiment classification (Negative, Neutral, Positive)."
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)
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interface.launch(share=True)
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