Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,3 +1,5 @@
+'''
+
 import altair as alt
 import numpy as np
 import pandas as pd
@@ -37,4 +39,36 @@ st.altair_chart(alt.Chart(df, height=700, width=700)
         y=alt.Y("y", axis=None),
         color=alt.Color("idx", legend=None, scale=alt.Scale()),
         size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    ))
+
+'''
+
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+st.set_page_config(page_title="FinBERT Sentiment", layout="centered")
+st.title("💰 FinBERT: Financial Sentiment Analysis")
+st.markdown("Модель: `yiyanghkust/finbert-tone` — обучена на финансовых текстах")
+
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("yiyanghkust/finbert-tone")
+    model = AutoModelForSequenceClassification.from_pretrained("yiyanghkust/finbert-tone")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Ввод текста
+text = st.text_area("Введите финансовую новость, заголовок или отчёт:", height=150)
+
+if st.button("Анализировать тональность") and text.strip():
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = F.softmax(outputs.logits, dim=1).squeeze()
+
+    labels = ["📉 Negative", "😐 Neutral", "📈 Positive"]
+    for label, prob in zip(labels, probs):
+        st.write(f"**{label}:** {prob.item():.3f}")