app.py

import streamlit as st
import torch

# Model class
class FinancialBERT(torch.nn.Module):
    def __init__(self, model_path):
        super(FinancialBERT, self).__init__()
        self.bert = torch.load(model_path)
        
    def forward(self, input_ids, attention_mask):
        output = self.bert(input_ids, attention_mask=attention_mask)
        return output.loss, output.logits

# Load model
MODEL_PATH = "Sandy0909/finance_sentiment"
model = FinancialBERT(MODEL_PATH)
model.eval()

# Streamlit App
st.title("Financial Sentiment Analysis")
sentence = st.text_area("Enter a financial sentence:", "")
if st.button("Predict"):
    # Here, you'll need some way to tokenize the input sentence and turn it into tensors.
    # This part has been removed in the provided code.
    # inputs = ...
    with torch.no_grad():
        logits = model(**inputs)[1]
    probs = torch.nn.functional.softmax(logits, dim=-1)
    predictions = torch.argmax(probs, dim=-1)
    sentiment = ['negative', 'neutral', 'positive'][predictions[0].item()]
    st.write(f"The predicted sentiment is: {sentiment}")