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| from transformers import pipeline | |
| import gradio as gr | |
| #load the model directly | |
| # Use a pipeline as a high-level helper | |
| pipe = pipeline("text-classification", model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis") | |
| #run the application | |
| demo=gr.Interface.from_pipeline(pipe) | |
| demo.launch() | |
| # import gradio as gr | |
| # from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| # import torch | |
| # # Load the pre-trained model and tokenizer | |
| # tokenizer = AutoTokenizer.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis") | |
| # model = AutoModelForSequenceClassification.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis") | |
| # # Define a function for sentiment analysis | |
| # def predict_sentiment(text): | |
| # # Tokenize the input text and prepare it to be used by the model | |
| # inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True) | |
| # # Forward pass through the model | |
| # with torch.no_grad(): | |
| # outputs = model(**inputs) | |
| # # Get the predicted probabilities and convert them to percentages | |
| # probabilities = torch.softmax(outputs.logits, dim=1).squeeze().tolist() | |
| # positive_percent = probabilities[2] * 100 | |
| # negative_percent = probabilities[0] * 100 | |
| # neutral_percent = probabilities[1] * 100 | |
| # # Construct the result dictionary | |
| # result = { | |
| # "Positive": round(positive_percent, 2), | |
| # "Negative": round(negative_percent, 2), | |
| # "Neutral": round(neutral_percent, 2) | |
| # } | |
| # return result | |
| # # Define inputs and outputs directly without using gr.inputs or gr.outputs | |
| # iface = gr.Interface( | |
| # fn=predict_sentiment, | |
| # inputs=gr.inputs.Textbox(lines=10, label="Enter financial statement"), | |
| # outputs=gr.outputs.Label(num_top_classes=3, label="Sentiment Percentages"), | |
| # title="Financial Statement Sentiment Analysis", | |
| # description="Predict the sentiment percentages of a financial statement." | |
| # ) | |
| # if __name__ == "__main__": | |
| # iface.launch() | |