Update src/streamlit_app.py

src/streamlit_app.py

@@ -41,47 +41,9 @@ st.altair_chart(alt.Chart(df, height=700, width=700)
         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
-import os
-
-st.set_page_config(page_title="💰 FinBERT: Financial Sentiment Analysis", layout="centered")
-st.title("💰 FinBERT: Financial Sentiment Analysis")
-st.markdown("Модель: `yiyanghkust/finbert-tone` — обучена на финансовых текстах")
-
-@st.cache_resource
-def load_model():
-    # Установка кастомного пути к кэшу
-    cache_dir = "/tmp/huggingface"
-    os.makedirs(cache_dir, exist_ok=True)
-
-    tokenizer = AutoTokenizer.from_pretrained("yiyanghkust/finbert-tone", cache_dir=cache_dir)
-    model = AutoModelForSequenceClassification.from_pretrained("yiyanghkust/finbert-tone", cache_dir=cache_dir)
-    return tokenizer, model
-
-tokenizer, model = load_model()
-
-text = st.text_area("Введите финансовую новость 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}")
 
 
 
-'''
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
@@ -445,3 +407,39 @@ st.markdown("""
 
 '''
 
+
+
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+import os
+
+st.set_page_config(page_title="💰 FinBERT: Financial Sentiment Analysis", layout="centered")
+st.title("💰 FinBERT: Financial Sentiment Analysis")
+st.markdown("Модель: `yiyanghkust/finbert-tone` — обучена на финансовых текстах")
+
+@st.cache_resource
+def load_model():
+    # Установка кастомного пути к кэшу
+    cache_dir = "/tmp/huggingface"
+    os.makedirs(cache_dir, exist_ok=True)
+
+    tokenizer = AutoTokenizer.from_pretrained("yiyanghkust/finbert-tone", cache_dir=cache_dir)
+    model = AutoModelForSequenceClassification.from_pretrained("yiyanghkust/finbert-tone", cache_dir=cache_dir)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+text = st.text_area("Введите финансовую новость 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}")
+