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NLP_Project_Sentiment_Analysis_2 / app.py

Updating app.py file

cf56017 verified over 1 year ago

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

# Defining the model path relative to the repository root.
#model_path = "abdullah123456/NLP_Project"
#model_path = "nlptown/bert-base-multilingual-uncased-sentiment"
model_path = "abdullah123456/NLP_Project_SA"

# Loading the model and tokenizer from the local directory.
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)


# Defining a simple text cleaning function
def clean_text(text):
    return " ".join(text.split())
    

# Defining the prediction function that the web interface will use.
def predict_sentiment(tweet: str) -> str:

    # Cleaning the tweet
    tweet_clean = clean_text(tweet)

    # Tokenizing the tweet. 
    inputs = tokenizer(tweet_clean, return_tensors="pt", truncation=True, padding="max_length", max_length=128)

    # Moving the input tensors to the same device as the model.
    inputs = {k: v.to(model.device) for k, v in inputs.items()}

    with torch.no_grad():
        outputs = model(**inputs)
    # Getting the predicted class index.
    predicted_class = torch.argmax(outputs.logits, dim=1).item()
    
    # Defining label mapping.
    label_mapping = {0: "negative", 1: "neutral", 2: "positive"}
    return label_mapping.get(predicted_class, "unknown")

# Creating the Gradio Interface.
iface = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(lines=4, placeholder="Enter an Urdu tweet here...", label="Urdu Tweet"),
    outputs=gr.Textbox(label="Predicted Sentiment"),
    title="Urdu Tweet Sentiment Analysis - 2",
    description="This app uses a fine-tuned transformer model to predict the sentiment of Urdu tweets. "
                "Enter your tweet in the textbox below and click 'Submit' to see the prediction.",
    examples=[
        ["السلام علیکم! آج کا دن بہت خوبصورت ہے۔"],
        ["میں بہت غمگین ہوں، دل بہت دکھ رہا ہے۔"],
        ["آپ کا کام بہت اچھا ہے!"]
    ]
)

# Launching the interface.
if __name__ == "__main__":
    iface.launch()

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

	# Defining the model path relative to the repository root.
	#model_path = "abdullah123456/NLP_Project"
	#model_path = "nlptown/bert-base-multilingual-uncased-sentiment"
	model_path = "abdullah123456/NLP_Project_SA"

	# Loading the model and tokenizer from the local directory.
	model = AutoModelForSequenceClassification.from_pretrained(model_path)
	tokenizer = AutoTokenizer.from_pretrained(model_path)


	# Defining a simple text cleaning function
	def clean_text(text):
	return " ".join(text.split())


	# Defining the prediction function that the web interface will use.
	def predict_sentiment(tweet: str) -> str:

	# Cleaning the tweet
	tweet_clean = clean_text(tweet)

	# Tokenizing the tweet.
	inputs = tokenizer(tweet_clean, return_tensors="pt", truncation=True, padding="max_length", max_length=128)

	# Moving the input tensors to the same device as the model.
	inputs = {k: v.to(model.device) for k, v in inputs.items()}

	with torch.no_grad():
	outputs = model(**inputs)
	# Getting the predicted class index.
	predicted_class = torch.argmax(outputs.logits, dim=1).item()

	# Defining label mapping.
	label_mapping = {0: "negative", 1: "neutral", 2: "positive"}
	return label_mapping.get(predicted_class, "unknown")

	# Creating the Gradio Interface.
	iface = gr.Interface(
	fn=predict_sentiment,
	inputs=gr.Textbox(lines=4, placeholder="Enter an Urdu tweet here...", label="Urdu Tweet"),
	outputs=gr.Textbox(label="Predicted Sentiment"),
	title="Urdu Tweet Sentiment Analysis - 2",
	description="This app uses a fine-tuned transformer model to predict the sentiment of Urdu tweets. "
	"Enter your tweet in the textbox below and click 'Submit' to see the prediction.",
	examples=[
	["السلام علیکم! آج کا دن بہت خوبصورت ہے۔"],
	["میں بہت غمگین ہوں، دل بہت دکھ رہا ہے۔"],
	["آپ کا کام بہت اچھا ہے!"]
	]
	)

	# Launching the interface.
	if __name__ == "__main__":
	iface.launch()