Added app.py and requirements.txt

app.py

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+import streamlit as st
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+model_name = "yiyanghkust/finbert-tone"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name)
+
+def analyze_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+    
+    sentiment = torch.argmax(logits, dim=1).item()
+    
+    if sentiment == 0:
+        return "Negative"
+    elif sentiment == 1:
+        return "Neutral"
+    else:
+        return "Positive"
+
+st.title("FinBERT Sentiment Analysis")
+st.write(
+    "This app uses FinBERT model to analyze sentiment of financial texts. "
+    "Enter text below to get its sentiment classification."
+)
+
+text_input = st.text_area("Enter your text here:")
+
+if text_input:
+    sentiment = analyze_sentiment(text_input)
+    st.write(f"Sentiment: {sentiment}")

requirements.txt

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+transformers>=4.0.0
+torch>=1.10.0
+streamlit>=1.0.0