Spaces:
Runtime error
Runtime error
Update src/streamlit_app.py
Browse files- src/streamlit_app.py +8 -4
src/streamlit_app.py
CHANGED
|
@@ -47,6 +47,7 @@ import streamlit as st
|
|
| 47 |
from transformers import AutoTokenizer, AutoModelForSequenceClassification
|
| 48 |
import torch
|
| 49 |
import torch.nn.functional as F
|
|
|
|
| 50 |
|
| 51 |
st.set_page_config(page_title="FinBERT Sentiment", layout="centered")
|
| 52 |
st.title("💰 FinBERT: Financial Sentiment Analysis")
|
|
@@ -54,14 +55,17 @@ st.markdown("Модель: `yiyanghkust/finbert-tone` — обучена на ф
|
|
| 54 |
|
| 55 |
@st.cache_resource
|
| 56 |
def load_model():
|
| 57 |
-
|
| 58 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 59 |
return tokenizer, model
|
| 60 |
|
| 61 |
tokenizer, model = load_model()
|
| 62 |
|
| 63 |
-
|
| 64 |
-
text = st.text_area("Введите финансовую новость, заголовок или отчёт:", height=150)
|
| 65 |
|
| 66 |
if st.button("Анализировать тональность") and text.strip():
|
| 67 |
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
|
|
|
|
| 47 |
from transformers import AutoTokenizer, AutoModelForSequenceClassification
|
| 48 |
import torch
|
| 49 |
import torch.nn.functional as F
|
| 50 |
+
import os
|
| 51 |
|
| 52 |
st.set_page_config(page_title="FinBERT Sentiment", layout="centered")
|
| 53 |
st.title("💰 FinBERT: Financial Sentiment Analysis")
|
|
|
|
| 55 |
|
| 56 |
@st.cache_resource
|
| 57 |
def load_model():
|
| 58 |
+
# Установка кастомного пути к кэшу
|
| 59 |
+
cache_dir = "/tmp/huggingface"
|
| 60 |
+
os.makedirs(cache_dir, exist_ok=True)
|
| 61 |
+
|
| 62 |
+
tokenizer = AutoTokenizer.from_pretrained("yiyanghkust/finbert-tone", cache_dir=cache_dir)
|
| 63 |
+
model = AutoModelForSequenceClassification.from_pretrained("yiyanghkust/finbert-tone", cache_dir=cache_dir)
|
| 64 |
return tokenizer, model
|
| 65 |
|
| 66 |
tokenizer, model = load_model()
|
| 67 |
|
| 68 |
+
text = st.text_area("Введите финансовую новость или отчёт:", height=150)
|
|
|
|
| 69 |
|
| 70 |
if st.button("Анализировать тональность") and text.strip():
|
| 71 |
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
|