Create app.py

app.py

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+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
+
+# Unique Page config
+st.set_page_config(
+    page_title="VisionGuard: AI Mask Monitor",
+    layout="wide"
+)
+
+# 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 with your branding
+with st.sidebar:
+    st.title("VisionGuard: Mask Monitor")
+    st.markdown("""
+    **VisionGuard** is a real-time AI system designed to help enforce health safety by detecting face mask compliance from images or webcam input.
+
+    **How it works:**
+    - Upload or capture an image
+    - The AI detects face(s) and checks for mask presence
+    - Results include confidence and visual feedback
+
+    This tool was built with a vision to promote public health and awareness using AI.
+
+    **Built by:** Thirupathirao • 2025
+    """)
+    st.info("For best results, use clear, front-facing images with good lighting.")
+    st.caption("Empowering safety through AI.")
+
+# Resize image
+def resize_image(image, max_size=(400, 400)):
+    image = image.copy()
+    image.thumbnail(max_size)
+    return image
+
+# Prediction logic
+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 header
+st.markdown("<h1 style='text-align: center;'>VisionGuard: AI Mask Monitor</h1>", unsafe_allow_html=True)
+st.markdown("<p style='text-align: center;'>A deep learning-powered tool for mask detection and safety awareness.</p>", unsafe_allow_html=True)
+
+# Input method
+input_choice = st.selectbox("Choose Input Method", ["Upload Image", "Use Webcam"])
+
+# Upload option
+if input_choice == "Upload Image":
+    uploaded_file = st.file_uploader("Upload an image (JPG, JPEG, PNG)", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file:
+        image_input = Image.open(uploaded_file)
+        resized_display = resize_image(image_input)
+
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.image(resized_display, caption="Uploaded Image")
+
+        with st.spinner("Analyzing with VisionGuard..."):
+            result_img, confidence, label = detect_and_predict(image_input)
+
+        with col2:
+            st.image(result_img, caption="Detection Output")
+            if confidence is not None:
+                st.metric("Confidence Score", f"{confidence*100:.2f}%")
+                if "Mask" in label:
+                    st.success("✔️ You're following safety measures!")
+                else:
+                    st.error("⚠️ No mask detected! Please wear a mask in public spaces.")
+            else:
+                st.warning(label)
+
+# Webcam option
+elif input_choice == "Use Webcam":
+    col1, col2 = st.columns([1, 3])
+
+    with col1:
+        camera_image = st.camera_input("Take a picture using your webcam")
+
+    if 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("Analysis Result")
+            st.image(result_img, caption="Detection Output")
+            if confidence is not None:
+                st.metric("Confidence Score", f"{confidence*100:.2f}%")
+                if "Mask" in label:
+                    st.success("✔️ You're following safety measures!")
+                else:
+                    st.error("⚠️ No mask detected! Please wear a mask in public spaces.")
+            else:
+                st.warning(label)