| 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) | |