Create app.py

app.py

@@ -0,0 +1,19 @@
+import tensorflow as tf
+import gradio as gr
+
+modelo =tf.keras.models.load_model('Models_flowers/model_0.h5')
+#modelo.load_weights("Models_flowers/model_0_weights.h5")
+classes=['daisy','dandelion','roses','sunflowers','tulips']
+
+def classifier(image): 
+  pred_img = model.predict(tf.expand_dims(image,axis=0))
+  pred_img = tf.squeeze(tf.round(pred_img))
+  texto = f'Predicted label: {class_id[(np.argmax(pred_img))]}'
+  return texto
+
+interface = gr.Interface(fn=classifier,
+                          gr.inputs.Image(shape=(1024,1024)),
+                          outputs = "text",
+                          description="classifier of images of daisy plants, dandelion, roses, sunflowers, and tulips.")
+interface.launch(inline=False)
+