Create app.py

app.py is added

app.py

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+import streamlit as st
+from transformers import BertTokenizer, BertForSequenceClassification, AdamW
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+
+# Config class
+class Config:
+    BERT_PATH = "ahmedrachid/FinancialBERT"
+    MODEL_PATH = "model.bin"
+    TRAIN_BATCH_SIZE = 32
+    VALID_BATCH_SIZE = 32
+    EPOCHS = 10
+    MAX_LEN = 512
+    TOKENIZER = BertTokenizer.from_pretrained(BERT_PATH)
+
+# FinancialBERT model class
+class FinancialBERT(nn.Module):
+    def __init__(self):
+        super(FinancialBERT, self).__init__()
+        self.bert = BertForSequenceClassification.from_pretrained(Config.BERT_PATH, num_labels=3, hidden_dropout_prob=0.5)
+        
+    def forward(self, input_ids, attention_mask, labels=None):
+        output = self.bert(input_ids, attention_mask=attention_mask, labels=labels)
+        return output.loss, output.logits
+
+# Load model
+model = FinancialBERT()
+model.load_state_dict(torch.load(Config.MODEL_PATH, map_location=torch.device('cpu')))
+model.eval()
+
+# Tokenizer
+tokenizer = Config.TOKENIZER
+
+def predict_sentiment(sentences):
+    inputs = tokenizer(sentences, return_tensors="pt", truncation=True, padding=True, max_length=Config.MAX_LEN)
+    with torch.no_grad():
+        logits = model(**inputs)[1]
+    probs = torch.nn.functional.softmax(logits, dim=-1)
+    predictions = torch.argmax(probs, 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}")