Update app.py

app.py

@@ -1,20 +1,12 @@
 import streamlit as st
-from transformers import pipeline
-from PIL import Image
+import tl_calamancy_lg
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+st.title("TagaCare")
 
-st.title("Hot Dog? Or Not?")
+nlp = tl_calamancy_lg.load()
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
+doc = nlp("what")
 
-if file_name is not None:
-    col1, col2 = st.columns(2)
+st.success(doc)
 
-    image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
-    predictions = pipeline(image)
 
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")