Update app.py

app.py

@@ -4,7 +4,7 @@ from transformers import AutoModel, AutoTokenizer
 from PIL import Image
 import uuid
 
-# Cache the model loading function
+# Cache the model loading function using @st.cache_resource
 @st.cache_resource
 def load_model(model_name):
     if model_name == "OCR for english or hindi (runs on CPU)":
@@ -18,6 +18,7 @@ def load_model(model_name):
     return tokenizer, model
 
 # Function to run the GOT model for multilingual OCR
+@st.cache_data
 def run_GOT(image, tokenizer, model):
     unique_id = str(uuid.uuid4())
     image_path = f"{unique_id}.png"
@@ -38,7 +39,12 @@ def run_GOT(image, tokenizer, model):
 # Function to highlight keyword in text
 def highlight_keyword(text, keyword):
     if keyword:
-        highlighted_text = text.replace(keyword, f"<mark>{keyword}</mark>")
+        # Use a case-insensitive search for highlighting
+        highlighted_text = text
+        highlighted_text = highlighted_text.replace(keyword, f"<mark>{keyword}</mark>")
+        highlighted_text = highlighted_text.replace(keyword.lower(), f"<mark>{keyword.lower()}</mark>")
+        highlighted_text = highlighted_text.replace(keyword.upper(), f"<mark>{keyword.upper()}</mark>")
+        highlighted_text = highlighted_text.replace(keyword.capitalize(), f"<mark>{keyword.capitalize()}</mark>")
         return highlighted_text
     return text
 
@@ -66,6 +72,8 @@ if uploaded_image:
             with st.spinner("Processing..."):
                 # Load the selected model (cached using @st.cache_resource)
                 tokenizer, model = load_model(model_option)
+                
+                # Run OCR and cache the result using @st.cache_data
                 result_text = run_GOT(image, tokenizer, model)
                 
                 if "Error" not in result_text: