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import gradio as gr
import transformers as pipeline
from transformers import AutoTokenizer,AutoModelForSequenceClassification
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "Sonny4Sonnix/twitter-roberta-base-sentimental-analysis-of-covid-tweets" # Replace with the name of the pre-trained model you want to use
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
sentiment = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
def get_sentiment(input_text):
return sentiment(input_text)
#Function to predict sentiments from the input text using the model
prediction = model.predict([text])[0]
if label==-1:
return "Negative"
elif label== 0:
return "Neutral"
else:
return "Positive"
iface = gr.Interface(fn=get_sentiment,title="Sentimental Analysis", inputs="text",outputs="text")
iface.launch(inline=True)
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