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app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import PIL
+import tensorflow as tf
+
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.models import Sequential
+
+from keras.models import load_model
+model1 = load_model('model1.h5')
+def predict_image(img):
+  img_4d=img.reshape(-1,180,180,3)
+  prediction=model1.predict(img_4d)[0]
+  return {class_names[i]: float(prediction[i]) for i in range(5)}
+class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips'] 
+image = gr.inputs.Image(shape=(180,180))
+label = gr.outputs.Label(num_top_classes=5)
+
+gr.Interface(fn=predict_image, inputs=image, outputs=label,interpretation='default').launch(debug='True')