Create app.py

app.py

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+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, (150, 150))
+    img_resize = np.array(img_resize, dtype=np.float32)
+    img_resize /= 255.0
+    img_input = img_resize.reshape(1, 150, 150, 3)
+    prediction = model.predict(img_input)
+
+    if prediction[0][0] < 0.5:
+        return "It's a Dog 🐶"
+    else:
+        return "It's a Cat 🐱"
+    
+           
+st.title('Is it a Cat or Dog 🐶🐱')
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+if uploaded_image is not None:
+    image = Image.open(uploaded_image)
+    
+    image_path = os.path.join(upload_folder, uploaded_image.name)
+    image.save(image_path)
+    output = get_result(image_path)
+    
+    st.write(output)
+    st.image(image, use_container_width=True)