Update app.py

app.py

@@ -2,14 +2,9 @@ import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
 import os
-import PIL
 import tensorflow as tf
-
-from tensorflow import keras
-from tensorflow.keras import layers
-from tensorflow.keras.models import Sequential
-
-from PIL import Image
+from tensorflow.keras import layers, Sequential
+from tensorflow.keras.models import load_model
 import gdown
 import zipfile
 import pathlib
@@ -42,20 +37,7 @@ except zipfile.BadZipFile:
 os.remove(local_zip_file)
 
 # Convert the extracted directory path to a pathlib.Path object
-data_dir = pathlib.Path(extracted_path)
-
-# Print the directory structure to debug
-for root, dirs, files in os.walk(extracted_path):
-    level = root.replace(extracted_path, '').count(os.sep)
-    indent = ' ' * 4 * (level)
-    print(f"{indent}{os.path.basename(root)}/")
-    subindent = ' ' * 4 * (level + 1)
-    for f in files:
-        print(f"{subindent}{f}")
-
-# Path to the dataset directory
-data_dir = pathlib.Path('extracted_files/Pest_Dataset')
-data_dir = pathlib.Path(data_dir)
+data_dir = pathlib.Path(extracted_path) / 'Pest_Dataset'
 
 # Verify if the path exists
 assert data_dir.exists(), f"Path {data_dir} does not exist."
@@ -95,7 +77,7 @@ for images, labels in train_ds.take(1):
     plt.axis("off")
 
 # Define data augmentation
-data_augmentation = keras.Sequential(
+data_augmentation = Sequential(
   [
     layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
     layers.RandomRotation(0.1),
@@ -116,7 +98,7 @@ for images, _ in train_ds.take(1):
 num_classes = len(class_names)
 model = Sequential([
   data_augmentation,
-  layers.Rescaling(1./255),
+  layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
   layers.Conv2D(16, 3, padding='same', activation='relu'),
   layers.MaxPooling2D(),
   layers.Conv2D(32, 3, padding='same', activation='relu'),