Update app.py

app.py

@@ -1,7 +1,15 @@
 import gradio as gr
-from transformers import pipeline
+#from transformers import pipeline
+from tensorflow.keras.models import load_model
 
-pipe = pipeline(task="image-classification", model="SuperSecureHuman/Flower-CNN")
+#pipe = pipeline(task="image-classification", model="SuperSecureHuman/Flower-CNN")
+
+model=load_model('./model.h5')
+
+def predict_image(img):
+    img_4d = img.reshape(-1,300,300,3)
+    prediction = model.predict(img_4d)[0]
+    return {class_names[i]: float(prediction[i]) for i in range(5)}
 
 class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
 
@@ -9,10 +17,11 @@ image = gr.inputs.Image(shape=(300,300))
 
 label = gr.outputs.Label(num_top_classes=5)
 
-gr.Interface.from_pipeline(pipe, 
+gr.Interface.from_pipeline(fn=predict_image, 
                            title="Flower Classification",
                            description="Flower CNN",
                            inputs = image,
                            outputs = label,
                            live=True,
+                           interpretation='default',
                            allow_flagging="never").launch()