create app.py

app.py

@@ -0,0 +1,47 @@
+import streamlit as st
+from PIL import Image
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+import os
+# Ensure the 'upload' directory exists
+upload_folder = 'uploads'
+if not os.path.exists(upload_folder):
+    os.makedirs(upload_folder)
+    
+# Load the pre-trained model
+model = load_model("gender_detector.keras")
+
+def get_result(img_path):
+    img = cv2.imread(img_path)
+    img_resize = cv2.resize(img, (224, 224))
+    img_resize = np.array(img_resize, dtype=np.float32)
+    img_resize /= 255.0
+    img_input = img_resize.reshape(1, 224, 224, 3)
+    prediction = model.predict(img_input)
+
+    if prediction[0][0] < 0.5:
+        return "He is a Men."
+    else:
+        return "She is a Women."
+    
+           
+# Set the title of the app
+st.title('Image Input and Display')
+
+# Upload image
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+# If an image is uploaded, display it along with text
+if uploaded_image is not None:
+    # Open the image using PIL
+    
+    # output = get_result(uploaded_image)
+    
+    image = Image.open(uploaded_image)
+    
+    image_path = os.path.join(upload_folder, uploaded_image.name)
+    image.save(image_path)
+    output = get_result(image_path)
+    # Display the image
+    st.image(image, caption= output, use_container_width=True)