Update app.py

app.py

@@ -1,151 +1,3 @@
-# import streamlit as st
-# import numpy as np
-# import cv2
-# from keras.models import load_model
-# from keras.preprocessing.image import img_to_array
-# from PIL import Image
-
-# # Page config
-# st.set_page_config(
-#     page_title="😷 Smart Face Mask Detection",
-#     layout="wide",
-#     page_icon="😷"
-# )
-
-# # Load model with caching
-# @st.cache_resource
-# def load_model_cached():
-#     return load_model("project_face_mask_detection.keras")
-
-# model = load_model_cached()
-
-# # Haar Cascade for face detection
-# face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
-
-# # Sidebar content
-# with st.sidebar:
-#     st.title("🧠 About This App")
-#     st.markdown("""
-#     This app uses deep learning to detect whether a person is wearing a face mask.
-    
-#     - Upload or capture an image.
-#     - Get instant feedback.
-#     - Built with Streamlit & Keras.
-#     """)
-#     st.info("Tip: Use well-lit images with clear faces for best results.")
-#     st.markdown("---")
-#     st.caption("📍 Developed by Surendra • 2025")
-
-# # Resize function
-# def resize_image(image, max_size=(400, 400)):
-#     image = image.copy()
-#     image.thumbnail(max_size)  # Maintains aspect ratio
-#     return image
-
-# # Detection function
-# def detect_and_predict(image_input):
-#     image_np = np.array(image_input.convert("RGB"))
-#     gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
-#     faces = face_cascade.detectMultiScale(gray, 1.1, 4)
-
-#     if len(faces) == 0:
-#         return image_input, None, "⚠️ No face detected"
-
-#     x, y, w, h = faces[0]
-#     face_roi = image_np[y:y+h, x:x+w]
-#     face_pil = Image.fromarray(face_roi).resize((200, 200))
-#     img_array = img_to_array(face_pil) / 255.0
-#     img_array = np.expand_dims(img_array, axis=0)
-
-#     prediction = model.predict(img_array)[0][0]
-#     confidence = (1 - prediction) if prediction < 0.5 else prediction
-#     label = "✅ Mask Detected" if prediction < 0.5 else "🚫 No Mask Detected"
-
-#     # Draw results
-#     color = (0, 255, 0) if prediction < 0.5 else (255, 0, 0)
-#     cv2.rectangle(image_np, (x, y), (x + w, y + h), color, 2)
-#     cv2.putText(image_np, f"{label} ({confidence*100:.2f}%)", (x, y - 10),
-#                 cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
-
-#     return Image.fromarray(image_np), confidence, label
-
-# # App title
-# st.markdown("<h1 style='text-align: center;'>😷 AI Face Mask Detection System</h1>", unsafe_allow_html=True)
-# st.markdown("<p style='text-align: center;'>Upload or capture an image to analyze mask presence.</p>", unsafe_allow_html=True)
-
-# # Input choice
-# input_choice = st.selectbox("Choose Input Method", ["📤 Upload Image", "📷 Use Webcam"])
-
-# if input_choice == "📤 Upload Image":
-#     uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
-    
-
-#     if uploaded_file:
-#         col1, col2 = st.columns(2)
-#         image_input = resize_image(Image.open(uploaded_file))
-#         image_np = np.array(image_input)
-#         resized_img = cv2.resize(image_np, (400, 400))
-#         resized_img_rgb = cv2.cvtColor(resized_img, cv2.COLOR_BGR2RGB)
-#         # resized_img =cv2.resize(image_input,(400,400)) #image_input.resize((400, 400))  # width=400, height=150
-#         with col1:
-#             # st.image(image_input, caption="Uploaded Image", width=400,height = 150)#use_container_width=True)
-#             # st.image(resized_img, caption="Uploaded Image")
-#             st.image(resized_img_rgb, caption="Uploaded Image")
-
-#         with st.spinner("Analyzing with AI model..."):
-#             result_img, confidence, label = detect_and_predict(image_input)
-
-#         # col1, col2 = st.columns(2)
-#         with col2:
-#             # resized_img = result_img.resize((400, 400)) 
-#             result_img_pil = Image.fromarray(result_img)
-#             resized_img = result_img_pil.resize((400, 400))
-            
-#             st.image(resized_img, caption="Detection Output")#, width=400,height = 150)#use_container_width=True)
-#         # with col2:
-#             if confidence is not None:
-#                 st.metric("Confidence Score", f"{confidence*100:.2f}%")
-#                 if "Mask" in label:
-#                     st.success(label)
-#                 else:
-#                     st.error(label)
-#             else:
-#                 st.warning(label)
-
-
-
-# elif input_choice == "📷 Use Webcam":
-#     col1, col2 = st.columns([1, 3])
-#     with col1:
-#         camera_image = st.camera_input("Take a picture using webcam")
-#     if camera_image:
-#         image_input = resize_image(Image.open(camera_image))
-#         # st.image(image_input, caption="Webcam Snapshot", use_container_width=True)
-
-#         with st.spinner("Analyzing..."):
-#             result_img, confidence, label = detect_and_predict(image_input)
-
-#         # col1, col2 = st.columns(2)
-#         with col2:
-#             st.write("Resulted Image")
-#             # resized_img = result_img.resize((400, 400))  # width=400, height=150
-#             # resized_img = result_img.resize((200, 200))
-#             result_img_pil = Image.fromarray(result_img)
-#             resized_img = result_img_pil.resize((400, 400))
-
-#             st.image(resized_img, caption="Detection Output")#, width=400,height = 150)
-#             # st.image(result_img, caption="Detection Output", use_container_width=True).resize(200,200)
-#         # with col2:
-#             if confidence is not None:
-#                 st.metric("Confidence Score", f"{confidence*100:.2f}%")
-#                 if "Mask" in label:
-#                     st.success(label)
-#                 else:
-#                     st.error(label)
-#             else:
-#                 st.warning(label)
-
-
 import streamlit as st
 import numpy as np
 import cv2
@@ -170,7 +22,7 @@ model = load_model_cached()
 # Haar Cascade for face detection
 face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 
-# Sidebar content
+# Sidebar
 with st.sidebar:
     st.title("🧠 About This App")
     st.markdown("""
@@ -184,10 +36,10 @@ with st.sidebar:
     st.markdown("---")
     st.caption("📍 Developed by Surendra • 2025")
 
-# Resize function
+# Optional resize function (only for uploads)
 def resize_image(image, max_size=(400, 400)):
     image = image.copy()
-    image.thumbnail(max_size)  # Maintains aspect ratio
+    image.thumbnail(max_size)  # maintains aspect ratio
     return image
 
 # Detection function
@@ -209,7 +61,7 @@ def detect_and_predict(image_input):
     confidence = (1 - prediction) if prediction < 0.5 else prediction
     label = "✅ Mask Detected" if prediction < 0.5 else "🚫 No Mask Detected"
 
-    # Draw results
+    # Draw results on original image
     color = (0, 255, 0) if prediction < 0.5 else (255, 0, 0)
     cv2.rectangle(image_np, (x, y), (x + w, y + h), color, 2)
     cv2.putText(image_np, f"{label} ({confidence*100:.2f}%)", (x, y - 10),
@@ -217,41 +69,38 @@ def detect_and_predict(image_input):
 
     return Image.fromarray(image_np), confidence, label
 
-# App title
+# App header
 st.markdown("<h1 style='text-align: center;'>😷 AI Face Mask Detection System</h1>", unsafe_allow_html=True)
 st.markdown("<p style='text-align: center;'>Upload or capture an image to analyze mask presence.</p>", unsafe_allow_html=True)
 
 # Input choice
 input_choice = st.selectbox("Choose Input Method", ["📤 Upload Image", "📷 Use Webcam"])
 
+# === Upload Image ===
 if input_choice == "📤 Upload Image":
     uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
-    
+
     if uploaded_file:
-        col1, col2 = st.columns(2)
-        image_input = resize_image(Image.open(uploaded_file))
+        image_input = Image.open(uploaded_file)
+        resized_display = resize_image(image_input)
 
-        # Convert to NumPy and resize with OpenCV
-        image_np = np.array(image_input)
-        resized_img = cv2.resize(image_np, (400, 400))
-        resized_img_rgb = cv2.cvtColor(resized_img, cv2.COLOR_BGR2RGB)
+        col1, col2 = st.columns(2)
 
         with col1:
-            st.image(resized_img_rgb, caption="Uploaded Image")
+            st.image(resized_display, caption="Uploaded Image")
 
         with st.spinner("Analyzing with AI model..."):
             result_img, confidence, label = detect_and_predict(image_input)
 
         with col2:
-            resized_output = result_img.resize((400, 400))
-            st.image(resized_output, caption="Detection Output")
-            
+            st.image(result_img, caption="Detection Output")
             if confidence is not None:
                 st.metric("Confidence Score", f"{confidence*100:.2f}%")
                 st.success(label) if "Mask" in label else st.error(label)
             else:
                 st.warning(label)
 
+# === Webcam Input ===
 elif input_choice == "📷 Use Webcam":
     col1, col2 = st.columns([1, 3])
 
@@ -259,16 +108,13 @@ elif input_choice == "📷 Use Webcam":
         camera_image = st.camera_input("Take a picture using webcam")
 
     if camera_image:
-        image_input = resize_image(Image.open(camera_image))
+        image_input = Image.open(camera_image)
 
         with st.spinner("Analyzing..."):
             result_img, confidence, label = detect_and_predict(image_input)
 
         with col2:
-            st.write("Resulted Image")
-            resized_output = result_img.resize((400, 400))
-            st.image(resized_output, caption="Detection Output")
-            
+            st.image(result_img, caption="Detection Output")
             if confidence is not None:
                 st.metric("Confidence Score", f"{confidence*100:.2f}%")
                 st.success(label) if "Mask" in label else st.error(label)