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Covid19_Sentimental_Analysis_App / app.py

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	from transformers import AutoTokenizer
	import gradio as gr
	from transformers import AutoModelForSequenceClassification, AutoTokenizer

	# Specifying the model path, which points to the Hugging Face Model Hub
	model_path = f'Mbabazi/cardiffnlp_twitter_roberta_base_sentiment_latest_Nov2023'
	tokenizer = AutoTokenizer.from_pretrained(model_path)
	model = AutoModelForSequenceClassification.from_pretrained(model_path)

	# Function to predict sentiment of a given tweet
	def predict_tweet(tweet):
	# Tokenize the input tweet using the specified tokenizer
	inputs = tokenizer(tweet, return_tensors="pt", padding=True, truncation=True, max_length=128)

	# Passing the tokenized input through the pre-trained sentiment analysis model
	outputs = model(**inputs)

	# Applying softmax to obtain probabilities for each sentiment class
	probs = outputs.logits.softmax(dim=-1)

	# Defining sentiment classes
	sentiment_classes = ['Negative', 'Neutral', 'Positive']

	# Creating a dictionary with sentiment classes as keys and their corresponding probabilities as values
	return {sentiment_classes[i]: float(probs.squeeze()[i]) for i in range(len(sentiment_classes))}


	# Create a Gradio Interface for the tweet sentiment prediction function
	iface = gr.Interface(
	fn=predict_tweet, # Set the prediction function
	inputs="text", # Specify input type as text
	outputs="label", # Specify output type as label
	title="Vaccine Sentiment Classifier", # Set the title of the interface
	description="Enter a text about vaccines to determine if the sentiment is negative, neutral, or positive.", # Provide a brief description
	examples=[
	["Vaccinations have been a game-changer in public health, significantly reducing the incidence of many dangerous diseases and saving countless lives."],
	["Vaccinations are a medical intervention that introduces a vaccine to stimulate an individual’s immune response against a particular disease."],
	["Vaccines are rushed to the market without proper testing and are pushed by corporations that value profits over the well-being of the public."]
	]
	)


	iface.launch()

	# with gr.Blocks() as demo: