Update app.py

app.py

@@ -1,35 +1,37 @@
-import streamlit as st
-import numpy as np
-from keras.models import load_model
-from keras.preprocessing import image
-from PIL import Image
-import os
-
-# Load the trained model
-model = load_model("Face Detector.keras")
-
-st.title("😷 Face Mask Detection App")
-st.write("Upload an image and check if the person is wearing a mask.")
-
-# File uploader
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    # Show uploaded image
-    img = Image.open(uploaded_file)
-    st.image(img, caption="Uploaded Image", use_column_width=True)
-
-    # Preprocess image
-    img = img.resize((200, 200))
-    img = image.img_to_array(img)
-    img = np.expand_dims(img, axis=0)
-    img = img / 255.0
-
-    # Predict
-    prediction = model.predict(img)[0][0]
-
-    # Result
-    if prediction < 0.5:
-        st.success("✅ Mask is Detected")
-    else:
-        st.error("🚫 Mask is NOT Detected")
+import gradio as gr
+import numpy as np
+from keras.models import load_model
+from keras.preprocessing import image
+from PIL import Image
+
+# Load model
+model = load_model("Face Detector.keras")
+
+# Prediction function
+def predict_mask(img):
+    if img is None:
+        return "No image provided"
+    
+    img = img.resize((200, 200))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0
+
+    pred = model.predict(img_array)[0][0]
+
+    if pred < 0.5:
+        return "✅ Mask is Detected"
+    else:
+        return "🚫 Mask is NOT Detected"
+
+# Interface
+iface = gr.Interface(
+    fn=predict_mask,
+    inputs=gr.Image(source="webcam", tool="editor", type="pil"),
+    outputs="text",
+    title="😷 Face Mask Detection App",
+    description="Use your webcam or upload an image to check if a face mask is present."
+)
+
+if __name__ == "__main__":
+    iface.launch()