Update app.py

app.py

@@ -48,7 +48,9 @@
 #         cv2.putText(frame, label, (x, y - 10),
 #                     cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
 
-#     return frame, confidence, plate_text
+#     # Ensure output image is same size as input
+#     result_img = Image.fromarray(frame)
+#     return result_img, confidence, plate_text
 
 # # Streamlit App UI
 # st.set_page_config(page_title="License Plate Detection", layout="wide")
@@ -65,11 +67,12 @@
 #         with col1:
 #             st.image(image_input, caption="Uploaded Image", use_container_width=True)
 
-#             if st.button("🔍 Detect from Upload"):
-#                 with st.spinner("Processing..."):
-#                     result_img, confidence, label = detect_and_predict(image_input)
+#         if st.button("🔍 Detect from Upload"):
+#             with st.spinner("Processing..."):
+#                 result_img, confidence, label = detect_and_predict(image_input)
+
 #             with col2:
-#                 st.image(result_img, caption="Detection Result", channels="RGB", use_container_width=True)
+#                 st.image(result_img, caption="Detection Result", use_container_width=True)
 #                 if confidence:
 #                     st.metric("Confidence", f"{confidence * 100:.2f}%")
 #                     st.success(f"Detected Text: {label}")
@@ -84,23 +87,18 @@
 #     if camera_image:
 #         try:
 #             image_input = Image.open(camera_image)
-#             # with col2:
-#             #     st.image(image_input, caption="Webcam Snapshot", use_container_width=True)
-
 #             with st.spinner("Analyzing..."):
 #                 result_img, confidence, label = detect_and_predict(image_input)
 #             with col2:
-#                 st.image(result_img, caption="Detection Result", channels="RGB", use_container_width=True)
+#                 st.image(result_img, caption="Detection Result", use_container_width=True)
 #                 if confidence is not None:
 #                     st.metric("Confidence", f"{confidence*100:.2f}%")
 #                     st.success(f"Detected Text: {label}")
 #                 else:
 #                     st.warning("Plate detected but no readable text found.")
-
 #         except Exception as e:
 #             st.error(f"❌ Error: {str(e)}")
 
-
 import streamlit as st
 import cv2
 import numpy as np
@@ -146,16 +144,17 @@ def detect_and_predict(img_input):
         else:
             label = "Plate Not Detected"
 
-        # Draw detection
         cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
         cv2.putText(frame, label, (x, y - 10),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
 
-    # Ensure output image is same size as input
+    # Convert back to PIL and resize to 450x450 for display
     result_img = Image.fromarray(frame)
+    result_img = result_img.resize((450, 450))
     return result_img, confidence, plate_text
 
-# Streamlit App UI
+
+# Streamlit UI
 st.set_page_config(page_title="License Plate Detection", layout="wide")
 st.title("🚘 License Plate Detection App")
 
@@ -164,23 +163,23 @@ tab1, tab2 = st.tabs(["📁 Upload Image", "📷 Webcam Capture"])
 # Tab 1 - Upload Image
 with tab1:
     col1, col2 = st.columns([1, 2])
-    uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
-    if uploaded_file:
-        image_input = Image.open(uploaded_file)
-        with col1:
+    with col1:
+        uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"], key="uploader")
+        if uploaded_file:
+            image_input = Image.open(uploaded_file)
             st.image(image_input, caption="Uploaded Image", use_container_width=True)
 
-        if st.button("🔍 Detect from Upload"):
-            with st.spinner("Processing..."):
-                result_img, confidence, label = detect_and_predict(image_input)
+            if st.button("🔍 Detect from Upload"):
+                with st.spinner("Processing..."):
+                    result_img, confidence, label = detect_and_predict(image_input)
 
-            with col2:
-                st.image(result_img, caption="Detection Result", use_container_width=True)
-                if confidence:
-                    st.metric("Confidence", f"{confidence * 100:.2f}%")
-                    st.success(f"Detected Text: {label}")
-                else:
-                    st.warning("No plate text detected.")
+                with col2:
+                    st.image(result_img, caption="Detection Result (450x450)", use_container_width=False)
+                    if confidence:
+                        st.metric("Confidence", f"{confidence * 100:.2f}%")
+                        st.success(f"Detected Text: {label}")
+                    else:
+                        st.warning("No plate text detected.")
 
 # Tab 2 - Webcam Input (camera snapshot)
 with tab2:
@@ -192,8 +191,9 @@ with tab2:
             image_input = Image.open(camera_image)
             with st.spinner("Analyzing..."):
                 result_img, confidence, label = detect_and_predict(image_input)
+
             with col2:
-                st.image(result_img, caption="Detection Result", use_container_width=True)
+                st.image(result_img, caption="Detection Result (450x450)", use_container_width=False)
                 if confidence is not None:
                     st.metric("Confidence", f"{confidence*100:.2f}%")
                     st.success(f"Detected Text: {label}")