Update app.py

change to CaBERT

app.py

@@ -1,24 +1,25 @@
 from transformers import pipeline, AutoTokenizer
 import streamlit as st
+# Access the secret token from HF secrets
+hf_token = os.getenv("HF_MODEL_TOKEN")
 
-@st.cache_resource
-def context_text(text): return f"### Context\n{text}\n\n### Answer"
+# Login to Hugging Face
+login(token=hf_token)
 
 @st.cache_resource
 def load_pipe():
-    model_name = "MSey/pbt_CaBERT_7_c10731"
+    model_name = "MSey/_table_CaBERT_0003_gbert-base_fl32_checkpoint-15852"
     return pipeline("token-classification", model=model_name), AutoTokenizer.from_pretrained(model_name)
 
 pipe, tokenizer = load_pipe()
 
-st.header("Test Environment for pbt_CaBERT_7_c10731")
+st.header("Test Environment for GBERT Ca Model")
 user_input = st.text_input("Enter your Prompt here:", "")
-contexted_ipnut = context_text(user_input)
-context_len = len(contexted_ipnut)
+
 
 if user_input:
     with st.spinner('Generating response...'):
-        response = pipe(contexted_ipnut)
+        response = pipe(user_input)
         st.write("Response:")
         tuples = ""
         # Process each entity and highlight the labeled words