Update app.py

app.py

@@ -5,8 +5,6 @@ import cv2
 import numpy as np
 from PIL import Image
 from tensorflow.keras.models import load_model
-import tempfile
-import os
 
 # Title for the Streamlit App
 st.title("Nepal Vehicle License Plate and Character Recognition")
@@ -95,32 +93,31 @@ if uploaded_file is not None:
     # Load image
     image = Image.open(uploaded_file)
 
-    # Detect license plates and characters
+    # Detect license plates
     with st.spinner("Processing image..."):
         cropped_plates, detected_image = detect_and_crop_license_plate(image)
 
-        # Show the image with detected license plates
-        st.image(cv2.cvtColor(detected_image, cv2.COLOR_BGR2RGB), caption="Detected License Plates", use_container_width=True)
-
         if cropped_plates:
+            st.image(cv2.cvtColor(detected_image, cv2.COLOR_BGR2RGB), caption="Detected License Plates", use_container_width=True)
             st.write(f"Detected {len(cropped_plates)} license plate(s).")
-            for idx, cropped_image in enumerate(cropped_plates, 1):
-                st.write(f"Processing License Plate {idx}:")
 
-                # Detect and crop characters
-                character_crops = detect_and_crop_characters(cropped_image)
+            for idx, cropped_plate in enumerate(cropped_plates, 1):
+                st.write(f"Processing License Plate {idx}:")
+                character_crops = detect_and_crop_characters(cropped_plate)
 
                 if character_crops:
-                    # Recognize characters
                     recognized_characters = recognize_characters(character_crops)
-
-                    # Show each cropped character and prediction
-                    for i, char_crop in enumerate(character_crops):
-                        st.image(cv2.cvtColor(char_crop, cv2.COLOR_BGR2RGB), caption=f"Character {i+1}")
-                        st.write(f"Predicted Character: {recognized_characters[i]}")
+                    st.write("Recognized Characters:", "".join(recognized_characters))
                 else:
                     st.write("No characters detected in this license plate.")
         else:
-            st.write("No license plates detected.")
+            st.write("No license plates detected. Running character detection on the full image.")
+            character_crops = detect_and_crop_characters(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR))
+
+            if character_crops:
+                recognized_characters = recognize_characters(character_crops)
+                st.write("Recognized Characters:", "".join(recognized_characters))
+            else:
+                st.write("No characters detected in the full image.")
 
     st.success("Processing complete!")