@@ -242,6 +326,19 @@ document.addEventListener('DOMContentLoaded', function() {
return div.innerHTML;
}
+ function getStarsHTML(rating) {
+ let starsHTML = '';
+ const roundedRating = Math.round(rating) || 5;
+ for (let i = 1; i <= 5; i++) {
+ if (i <= roundedRating) {
+ starsHTML += '
';
+ } else {
+ starsHTML += '
';
+ }
+ }
+ return `
${starsHTML}
`;
+ }
+
function formatDate(dateString) {
const date = new Date(dateString);
const now = new Date();
diff --git a/model.py b/model.py
index 24fc008f..fcf0f9b1 100644
--- a/model.py
+++ b/model.py
@@ -1,4 +1,9 @@
+import torch
import torch.nn as nn
+import os
+import logging
+
+logger = logging.getLogger(__name__)
class PlantDiseaseNet(nn.Module):
def __init__(self, num_classes=38): # original model had 38 classes
@@ -77,3 +82,34 @@ def forward(self, x):
x = self.pool(x)
x = self.classifier(x)
return x
+
+def load_pytorch_model(model_path, device='cpu'):
+ try:
+ if not os.path.exists(model_path):
+ logger.error(f"PyTorch model file missing: {model_path}")
+ return None
+ # Safe loading with torch.load
+ model = torch.load(model_path, map_location=device)
+ # If it was saved as state dict rather than full model
+ if isinstance(model, dict):
+ net = PlantDiseaseNet()
+ net.load_state_dict(model)
+ model = net
+ model.eval()
+ return model
+ except Exception as e:
+ logger.error(f"Error loading PyTorch model from {model_path}: {str(e)}")
+ return None
+
+def predict_pytorch(model, input_tensor):
+ if model is None:
+ logger.error("Prediction failed: PyTorch model is not loaded (None)")
+ return None
+ try:
+ with torch.no_grad():
+ output = model(input_tensor)
+ return output
+ except Exception as e:
+ logger.error(f"Error predicting with PyTorch model: {str(e)}")
+ return None
+
diff --git a/tests/conftest.py b/tests/conftest.py
index c74f8cbc..f4659a8c 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -8,11 +8,15 @@
# Provide lightweight stubs for optional heavy external modules so tests can import app
import types
if 'google.generativeai' not in sys.modules:
- google = types.ModuleType('google')
+ if 'google' in sys.modules:
+ google = sys.modules['google']
+ else:
+ google = types.ModuleType('google')
+ google.__path__ = []
+ sys.modules['google'] = google
genai = types.ModuleType('google.generativeai')
- google.generativeai = genai
- sys.modules['google'] = google
sys.modules['google.generativeai'] = genai
+ setattr(google, 'generativeai', genai)
# Import the Flask app from the project
try:
diff --git a/tests/test_predictions.py b/tests/test_predictions.py
index 01b34b8b..269ce765 100644
--- a/tests/test_predictions.py
+++ b/tests/test_predictions.py
@@ -48,3 +48,41 @@ def test_disease_predict_without_file(self, client):
response = client.post('/disease/predict')
# Should redirect to home when no file
assert response.status_code in [200, 302, 400]
+
+
+class TestMLModelErrorHandling:
+ """Test suite for ML model load and prediction error handling."""
+
+ def test_load_keras_model_missing_file(self):
+ """Test that load_keras_model returns None when file is missing."""
+ from utils import load_keras_model
+ assert load_keras_model("nonexistent_model.h5") is None
+
+ def test_predict_image_keras_none_model(self):
+ """Test that predict_image_keras returns controlled error response when model is None."""
+ from utils import predict_image_keras
+ pred, desc = predict_image_keras(None, "nonexistent_image.jpg")
+ assert pred == "Model unavailable"
+ assert "unavailable" in desc
+
+ def test_predict_image_keras_missing_image(self):
+ """Test that predict_image_keras handles missing image files gracefully when model is mocked."""
+ from utils import predict_image_keras
+ class MockModel:
+ def predict(self, x):
+ return [[0.9, 0.1]]
+
+ pred, desc = predict_image_keras(MockModel(), "nonexistent_image.jpg")
+ assert pred == "Image not found"
+ assert "could not be found" in desc
+
+ def test_load_pytorch_model_missing_file(self):
+ """Test that load_pytorch_model returns None when file is missing."""
+ from model import load_pytorch_model
+ assert load_pytorch_model("nonexistent_model.pth") is None
+
+ def test_predict_pytorch_none_model(self):
+ """Test that predict_pytorch returns None when model is None."""
+ from model import predict_pytorch
+ assert predict_pytorch(None, None) is None
+
diff --git a/utils.py b/utils.py
index 2789f1c6..44cf4e68 100644
--- a/utils.py
+++ b/utils.py
@@ -3,6 +3,9 @@
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import os
+import logging
+
+logger = logging.getLogger(__name__)
# Class labels (update if needed)
class_names = [
@@ -42,19 +45,34 @@
}
def load_keras_model(model_path):
- model = load_model(model_path)
- return model
-
-
+ try:
+ if not os.path.exists(model_path):
+ logger.error(f"Model file does not exist at path: {model_path}")
+ return None
+ model = load_model(model_path)
+ return model
+ except Exception as e:
+ logger.error(f"Failed to load Keras model from {model_path}: {str(e)}")
+ return None
def predict_image_keras(model, img_path):
- img = image.load_img(img_path, target_size=(160, 160)) # Match your training size
- img_array = image.img_to_array(img) / 255.0
- img_array = np.expand_dims(img_array, axis=0)
+ if model is None:
+ logger.error("Prediction failed: model is None")
+ return "Model unavailable", "The classification model is currently unavailable."
+ try:
+ if not os.path.exists(img_path):
+ logger.error(f"Image file does not exist at path: {img_path}")
+ return "Image not found", "The uploaded image file could not be found."
+ img = image.load_img(img_path, target_size=(160, 160)) # Match your training size
+ img_array = image.img_to_array(img) / 255.0
+ img_array = np.expand_dims(img_array, axis=0)
- predictions = model.predict(img_array)
- predicted_index = np.argmax(predictions)
- predicted_class = class_names[predicted_index]
- description = class_descriptions.get(predicted_class, "No description available.")
+ predictions = model.predict(img_array)
+ predicted_index = np.argmax(predictions)
+ predicted_class = class_names[predicted_index]
+ description = class_descriptions.get(predicted_class, "No description available.")
- return predicted_class, description
\ No newline at end of file
+ return predicted_class, description
+ except Exception as e:
+ logger.error(f"Error predicting image {img_path}: {str(e)}")
+ return "Model unavailable", f"An error occurred during prediction: {str(e)}"
\ No newline at end of file