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import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification, AutoModelForSequenceClassification
import gradio as gr
# Initialize sentiment analysis model and tokenizer
sentiment_tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
sentiment_model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")
sentiment_model.eval()
# Initialize stock identification model and tokenizer
ner_tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
ner_model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
ner_model.eval()
def get_advice(sentiment_label, stocks_mentioned):
# Add your own logic for providing advice based on sentiment and stocks mentioned
if sentiment_label == "Positive":
advice = "Positive sentiment. Consider taking advantage of positive market trends."
elif sentiment_label == "Negative":
if stocks_mentioned:
advice = f"Negative sentiment. Consider re-evaluating your position on stocks: {', '.join(stocks_mentioned)}."
else:
advice = "Negative sentiment. Consider monitoring the market for potential impacts."
else:
advice = "Neutral sentiment. The market may not be strongly influenced. Monitor for changes."
return sentiment_label
# return advice
def predict_sentiment_and_stock_info(headline):
# Sentiment Analysis
sentiment_inputs = sentiment_tokenizer(headline, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
sentiment_outputs = sentiment_model(**sentiment_inputs)
sentiment_prediction = torch.nn.functional.softmax(sentiment_outputs.logits, dim=-1)
pos, neg, neutr = sentiment_prediction[:, 0].item(), sentiment_prediction[:, 1].item(), sentiment_prediction[:, 2].item()
sentiment_label = "Positive" if pos > neg and pos > neutr else "Negative" if neg > pos and neg > neutr else "Neutral"
# Named Entity Recognition (NER)
ner_inputs = ner_tokenizer(headline, return_tensors="pt")
with torch.no_grad():
ner_outputs = ner_model(**ner_inputs)
# Identify stocks mentioned in the headline
ner_predictions = torch.nn.functional.softmax(ner_outputs.logits, dim=-1).argmax(2)
tokens = ner_tokenizer.convert_ids_to_tokens(ner_inputs['input_ids'][0].tolist()) # Use ner_inputs here
entities = ner_tokenizer.convert_ids_to_tokens(ner_predictions[0].tolist())
stocks_mentioned = [tokens[i] for i, entity in enumerate(entities) if entity.startswith("B")]
# Advice based on sentiment and identified stocks
advice = get_advice(sentiment_label, stocks_mentioned)
# return sentiment_label, advice
return sentiment_label
# Gradio Interface
'''iface = gr.Interface(
fn=predict_sentiment_and_stock_info,
inputs="text",
outputs=["text", "text"],
live=True,
title="Financial News Sentiment and Stock Analysis",
description="Enter a financial news headline to analyze its sentiment, identify mentioned stocks, and get advice on how to proceed."
)
iface.launch()'''
# Gradio Interface
iface = gr.Interface(
fn=predict_sentiment_and_stock_info,
inputs=[gr.Textbox(lines=2, label="Financial Statement")],
outputs=[
gr.Textbox(label="Sentiment"),
# gr.Textbox(label="Advice")
],
live=True,
title="Financial Content Sentiment Analysis",
description="Enter a financial statement to analyze its sentiment."
)
iface.launch() |