Update app.py

app.py

@@ -1,3 +1,4 @@
+!pip install gradio
 import gradio as gr
 import numpy as np
 
@@ -6,7 +7,9 @@ import tensorflow as tf
 from tensorflow import keras
 from tensorflow.keras import layers
 from tensorflow.keras.models import Sequential
-data_dir = "D:\\New Plant Diseases Dataset(Augmented)\\train"
+from google.colab import drive
+drive.mount('/content/drive')
+data_dir = "/content/drive/MyDrive/Colab Notebooks/valid"
 img_height,img_width=180,180
 batch_size=32
 train_ds = tf.keras.preprocessing.image_dataset_from_directory(
@@ -23,9 +26,6 @@ val_ds = tf.keras.preprocessing.image_dataset_from_directory(
   seed=123,
   image_size=(img_height, img_width),
   batch_size=batch_size)
-class_names = train_ds.class_names
-print(class_names)
-
 num_classes = 38
 
 model = Sequential([
@@ -50,8 +50,10 @@ history = model.fit(
   epochs=epochs
 )
 def predict_image(img):
-    img_4d=img.reshape(-1,180,180,3)
-    prediction=model.predict(img_4d)[0]
-    return {class_names[i]: float(prediction[i]) for i in range(38) 
+  img_4d=img.reshape(-1,180,180,3)
+  prediction=model.predict(img_4d)[0]
+  return {class_names[i]: float(prediction[i]) for i in range(5)}
+image = gr.inputs.Image(shape=(180,180))
+label = gr.outputs.Label(num_top_classes=5)
 
-gr.Interface(fn=predict_image, inputs=gr.inputs.Image(shape=(180,180)), outputs=gr.outputs.Label(num_top_classes=5),interpretation='default').launch(share = 'True', debug='True')
+gr.Interface(fn=predict_image, inputs=image, outputs=label,interpretation='default').launch(share = 'True',debug='True')