Update app.py

app.py

@@ -94,7 +94,7 @@ plt.legend(['Train', 'Validation'], loc='upper left')
 plt.show()
 
 # Assuming you have a test directory
-test_dir = '/content/drive/MyDrive/Tomato_Plant_Disease'  # Path to your test dataset
+test_dir = 'Tomato_Plant_Disease'  # Path to your test dataset
 
 # Create a test data generator (without augmentation)
 test_datagen = ImageDataGenerator(rescale=1./255)
@@ -110,7 +110,7 @@ test_generator = test_datagen.flow_from_directory(
 test_loss, test_acc = model.evaluate(test_generator, steps=test_generator.samples // 32)
 print(f"Test Accuracy: {test_acc}")
 
-model.save('/content/drive/MyDrive/tomato_disease_detection_model.h5')
+model.save('tomato_disease_detection_model.h5')
 
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing import image
@@ -118,7 +118,7 @@ import numpy as np
 from PIL import Image
 
 # Load the model
-model = load_model('/content/drive/MyDrive/tomato_disease_detection_model.h5')
+model = load_model('tomato_disease_detection_model.h5')
 
 def predict_disease(img_path):
     # Load and preprocess the image
@@ -138,7 +138,7 @@ def predict_disease(img_path):
         print("The plant is infected.")
 
 # Example usage:
-img_path = '/content/drive/MyDrive/Tomato_Plant_Disease/0/0045ba29-ed1b-43b4-afde-719cc7adefdb___GCREC_Bact.Sp 6254.JPG'
+img_path = 'Tomato_Plant_Disease/0/0045ba29-ed1b-43b4-afde-719cc7adefdb___GCREC_Bact.Sp 6254.JPG'
 predict_disease(img_path)
 
 import gradio as gr
@@ -147,7 +147,7 @@ from PIL import Image
 import numpy as np
 
 # Load the pre-trained model
-model = load_model('/content/drive/MyDrive/tomato_disease_detection_model.h5')
+model = load_model('tomato_disease_detection_model.h5')
 
 # Prediction function to be used in Gradio
 def predict_disease(img):