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Disaster-Tweet-Prediction-Using-XLNET / app.py

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	import gradio as gr
	import numpy as np
	import pandas as pd
	import tensorflow as tf
	from transformers import XLNetTokenizer, TFXLNetModel

	# Load your data from disaster_tweet.csv
	df = pd.read_csv("disaster_tweet.csv") # Update the filename here

	# Extract text and label columns
	text_data = df["text"]
	label_data = df["target"]

	# Load XLNet tokenizer and model
	xlnet_tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
	xlnet_model = TFXLNetModel.from_pretrained('xlnet-base-cased')

	# Define custom object scope for TFXLNetModel
	def load_model_with_custom_objects():
	with tf.keras.utils.custom_object_scope({"TFXLNetModel": TFXLNetModel}):
	model = tf.keras.models.load_model("xlnet_model.h5")
	return model

	# Load the saved model within custom object scope
	model = load_model_with_custom_objects()

	# Define function to predict disaster tweet
	def predict_disaster_tweet(text):
	input_ids = xlnet_tokenizer.encode(text, add_special_tokens=True, max_length=100, padding='max_length', return_tensors="tf")
	attention_masks = tf.ones_like(input_ids) # Assuming all tokens are relevant
	pred = model.predict([input_ids, attention_masks])
	final_pred = np.where(pred >= 0.5, 1, 0)
	if final_pred == 1:
	return "Disaster"
	elif final_pred == 0:
	return "Non-Disaster"
	else:
	return "Uncertain"

	# Define Gradio interface
	iface = gr.Interface(
	fn=predict_disaster_tweet,
	inputs="text",
	outputs="text",
	title="Disaster Tweet Prediction",
	description="Enter a tweet and get prediction whether it's a (Disaster, Non-Disaster, or Uncertain)."
	)

	# Launch the Gradio interface
	iface.launch(inline=False)