new update

sentiment_analysis.py

@@ -1,17 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-# Load pre-trained model and tokenizer
-#model_name = "ProsusAI/finbert"
-#model_name = "ahmedrachid/FinancialBERT-Sentiment-Analysis"
-model_name="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-# Example: Classify a financial text
-text = "With the launch of Apple Silicon, Apple shares have increased"
-inputs = tokenizer(text, return_tensors="pt")
-outputs = model(**inputs)
-predictions = outputs.logits.argmax(dim=1).item()
-
-# 'predictions' will contain the sentiment class (e.g., 0 for negative, 1 for neutral, 2 for positive)
-print("Predicted sentiment class:", predictions)