Update app.py

app.py

@@ -9,6 +9,7 @@ import torch
 import tempfile
 import os
 import re
+import base64
 from groq import Groq
 
 # Page configuration
@@ -65,7 +66,7 @@ def polish_text_with_ai(cleaned_text):
         ],
         model="gemma2-9b-it",
     )
-    polished_text=chat_completion.choices[0].message.content
+    polished_text = chat_completion.choices[0].message.content
     return polished_text
 
 # Extract text using GOT
@@ -106,6 +107,24 @@ model_choice = st.sidebar.selectbox("Select OCR Model:", ("GOT_CPU", "GOT_GPU",
 # Upload Section
 uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
 
+# Input from clipboard
+if st.sidebar.button("Paste from Clipboard"):
+    try:
+        clipboard_data = st.experimental_get_clipboard()
+        if clipboard_data:
+            # Assuming clipboard data is base64 encoded image
+            image_data = base64.b64decode(clipboard_data)
+            uploaded_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+            uploaded_file.write(image_data)
+            uploaded_file.seek(0)
+    except:
+        st.sidebar.warning("Clipboard data is not an image.")
+
+# Input from camera
+camera_file = st.sidebar.camera_input("Capture from Camera")
+if camera_file:
+    uploaded_file = camera_file
+
 # Predict button
 predict_button = st.sidebar.button("Predict")
 
@@ -151,10 +170,7 @@ if predict_button and uploaded_file:
         cleaned_text = clean_extracted_text(extracted_text)
 
         # Optionally, polish text with AI model for better language flow
-        if model_choice in ["GOT_CPU", "GOT_GPU"]:
-            polished_text = polish_text_with_ai(cleaned_text)
-        else:
-            polished_text = cleaned_text
+        polished_text = polish_text_with_ai(cleaned_text) if model_choice in ["GOT_CPU", "GOT_GPU"] else cleaned_text
 
         # Delete temp file
         if os.path.exists(temp_file_path):
@@ -166,14 +182,13 @@ if predict_button and uploaded_file:
 
         # Input box for real-time search
         search_query = st.text_input("Search in extracted text:", key="search_query", placeholder="Type to search...")
+
         # Update results dynamically based on the search term
         if search_query:
             # Highlight the search term in the text
             highlighted_text = highlight_text(polished_text, search_query)
             st.markdown("### Highlighted Search Results:")
-            # Render the highlighted text, allowing HTML rendering for the highlight
             st.markdown(highlighted_text, unsafe_allow_html=True)
         else:
-            # If no search term is provided, display the original text
             st.markdown("### Extracted Text:")
             st.markdown(polished_text)