app.py

app.py

@@ -0,0 +1,34 @@
+import cv2
+from tensorflow.keras.models import load_model
+import gradio as gr
+import tensorflow as tf
+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model
+
+# Load the pre-trained model
+new_model = load_model('cat_classifier_model.h5')
+
+def classify_image(image_path):
+    img = image.load_img(image_path, target_size=(224, 224))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.0  # Rescale to values between 0 and 1 (same as during training)
+
+    prediction = model.predict(img_array)
+    if prediction[0][0] > 0.5:
+        return "not a tablet"
+    else:
+        return "is a tablet"
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(),
+    outputs="text",
+    live=True,
+      
+)
+
+# Launch the Gradio interface
+iface.launch()