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