Update app.py

app.py

@@ -1,4 +1,3 @@
-
 from transformers import AutoModel, AutoTokenizer
 import streamlit as st
 from PIL import Image
@@ -21,7 +20,7 @@ def load_model(model_name):
     return model, tokenizer
 
 if "model" not in st.session_state or "tokenizer" not in st.session_state:
-    model, tokenizer = load_model("OCR for English or Hindi (CPU)")
+    model, tokenizer = load_model("OCR on CPU")
     st.session_state.update({"model": model, "tokenizer": tokenizer})
 
 # Function to run the GOT model for multilingual OCR
@@ -42,8 +41,8 @@ def highlight_text(text, search_term):
     return re.sub(re.escape(search_term), lambda m: f'<span style="background-color: red;">{m.group()}</span>', text, flags=re.IGNORECASE) if search_term else text
 
 # Streamlit App
-st.title(":blue[Object character recognition Application]")
-st.write("Give your Image")
+st.title("GOT-OCR Multilingual Demo")
+st.write("Upload an image for OCR")
 
 # Create two columns
 col1, col2 = st.columns(2)
@@ -59,7 +58,7 @@ with col1:
 with col2:
     model_option = st.selectbox("Select Model", ["OCR on CPU", "OCR on GPU"])
     
-    if st.button("DO OCR "):
+    if st.button("Run OCR"):
         if uploaded_image:
             with st.spinner("Processing..."):
                 model, tokenizer = load_model(model_option)
@@ -74,5 +73,4 @@ with col2:
     # Display the extracted text if it exists in session state
     if "extracted_text" in st.session_state:
         search_term = st.text_input("Enter a word or phrase to highlight:")
-        st.subheader("Extracted Text:")
-        st.markdown(f'<div style="white-space: pre-wrap;">{highlight_text(st.session_state["extracted_text"], search_term)}</div>', unsafe_allow_html=True)
+        st.subheader("Extracted Text:")