app.py

import streamlit as st
from transformers import BertTokenizer, BertForSequenceClassification
import torch

BERT_PATH = "ahmedrachid/FinancialBERT"
MODEL_PATH = "Sandy0909/finance_sentiment"
MAX_LEN = 512

# Initialize tokenizer and model
tokenizer = BertTokenizer.from_pretrained(BERT_PATH)
model = BertForSequenceClassification.from_pretrained(BERT_PATH, num_labels=3)
model.load_state_dict(torch.load(MODEL_PATH, map_location=torch.device('cpu')))
model.eval()

def predict_sentiment(sentence):
    inputs = tokenizer(sentence, return_tensors="pt", truncation=True, padding=True, max_length=MAX_LEN)
    with torch.no_grad():
        logits = model(**inputs).logits
    predictions = torch.argmax(logits, dim=-1)
    return ['negative', 'neutral', 'positive'][predictions[0].item()]

# Streamlit app
st.title("Financial Sentiment Analysis")
sentence = st.text_area("Enter a financial sentence:", "")
if st.button("Predict"):
    sentiment = predict_sentiment(sentence)
    st.write(f"The predicted sentiment is: {sentiment}")