app.py

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

# Config class
class Config:
    TOKENIZER_PATH = "ahmedrachid/FinancialBERT"  # Use tokenizer from the original model
    MODEL_PATH = "Sandy0909/finance_sentiment"
    MAX_LEN = 512
    TOKENIZER = BertTokenizer.from_pretrained(TOKENIZER_PATH)

class FinancialBERT(torch.nn.Module):
    def __init__(self):
        super(FinancialBERT, self).__init__()
        self.bert = BertForSequenceClassification.from_pretrained(Config.MODEL_PATH, num_labels=3, hidden_dropout_prob=0.5)
 
    def forward(self, input_ids, attention_mask, token_type_ids, labels=None):
        output = self.bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels)
        return output.loss, output.logits

# Load model
model = FinancialBERT()
model.eval()

# Streamlit App
# Set title and an image/banner if you have one
st.title("Financial Sentiment Analysis")
# st.image("path_to_your_image.jpg", use_column_width=True)

# Description
st.write("""
This application predicts the sentiment of financial sentences using a state-of-the-art model. Enter a financial sentence below and click 'Predict' to get its sentiment.
""")

sentence = st.text_area("Enter a financial sentence:", "")

if st.button("Predict"):
    tokenizer = Config.TOKENIZER
    inputs = tokenizer([sentence], return_tensors="pt", truncation=True, padding=True, max_length=Config.MAX_LEN)
    
    with torch.no_grad():
        logits = model(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], token_type_ids=inputs.get('token_type_ids'))[1]
    
    probs = torch.nn.functional.softmax(logits, dim=-1)
    predictions = torch.argmax(probs, dim=-1)
    sentiment = ['negative', 'neutral', 'positive'][predictions[0].item()]

    # Output visualization
    st.subheader('Predicted Sentiment:')
    st.write(f"The sentiment is: **{sentiment.capitalize()}**")

    # Show Confidence levels as a bar chart
    st.subheader('Model Confidence Levels:')
    st.bar_chart(probs[0].numpy(), use_container_width=True)

# Sidebar: Documentation/Help
st.sidebar.header('About')
st.sidebar.text("""
This application uses a BERT-based model trained specifically for financial sentences. The model can predict if the sentiment of a sentence is positive, negative, or neutral.
""")