feat: mvp front

app.py

@@ -1,29 +1,25 @@
-import os
-from pathlib import Path
-
 import streamlit as st
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
-# from huggingface_hub import login
-
-# login(os.getenv('HF_TOKEN'))
-
-# hub_folder = Path('~/.cache/huggingface/hub')
-# for path in hub_folder.walk():
-#     st.write(path)
-
 from transformers import AutoModelForSequenceClassification
 from transformers import AutoTokenizer
 from transformers import TextClassificationPipeline
 
-model = AutoModelForSequenceClassification.from_pretrained(
-    "issai/rembert-sentiment-analysis-polarity-classification-kazakh")
-tokenizer = AutoTokenizer.from_pretrained("issai/rembert-sentiment-analysis-polarity-classification-kazakh")
 
-pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
+@st.cache_data()
+def get_pipe():
+    model = AutoModelForSequenceClassification.from_pretrained(
+        "issai/rembert-sentiment-analysis-polarity-classification-kazakh")
+    tokenizer = AutoTokenizer.from_pretrained("issai/rembert-sentiment-analysis-polarity-classification-kazakh")
+    return TextClassificationPipeline(model=model, tokenizer=tokenizer)
+
 
-reviews = ["Бұл бейнефильм маған түк ұнамады.", "Осы кітап қызық сияқты."]
+pipe = get_pipe()
+st.title('KazSandra@ISSAI')
+st.text('Sentiment Analysis Polarity Classification In Kazakh Language.')
+input_text = st.text_area('Input text', placeholder='Provide your text', value='Осы кітап қызық сияқты.')
+# reviews = ["Бұл бейнефильм маған түк ұнамады.", "Осы кітап қызық сияқты."]
 
-for review in reviews:
-    st.write(pipe(review))
+# for review in reviews:
+if input_text:
+    out = pipe(input_text)[0]
+    st.text("Label: {label}\nScore: {score}".format(**out))

requirements.txt

@@ -1,5 +1,4 @@
 streamlit~=1.37.0
-huggingface-hub~=0.24.5
 torch==2.4.0
 torchvision==0.19.0
 torchaudio==2.4.0