Update app.py

app.py

@@ -7,32 +7,34 @@ import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
 from sklearn.metrics import confusion_matrix
+from urllib.parse import quote
+import requests
 
-# Make sure the model is loaded only once in the deployment environment
+# Ensure the model is loaded only once in the deployment environment
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# Assuming model path is provided by Hugging Face Spaces
-model_path = "./models"  # Store model weights inside the space directory
-os.makedirs(model_path, exist_ok=True)
-
-# Load the trained MobileNetV2 model from Hugging Face Hub
+# Model loading function
 def load_model():
     processor = AutoImageProcessor.from_pretrained("linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification")
     model = AutoModelForImageClassification.from_pretrained("linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification").to(device)
     model.eval()  # Set the model to evaluation mode
     return processor, model
 
+# Load model and processor
 processor, model = load_model()
 
-# Define the class names - These should match the labels in the dataset
+# Define the class names - Update with your actual class names
 class_names = ['Healthy', 'Disease_1', 'Disease_2', 'Disease_3']  # Update with your actual classes
 
+# Prediction function
 def predict(image):
     if isinstance(image, str):
         image = Image.open(image).convert("RGB")
     
+    # Preprocess the image
     inputs = processor(images=image, return_tensors="pt").to(device)
     
+    # Inference
     with torch.no_grad():
         logits = model(**inputs).logits
     predicted_class_idx = logits.argmax(-1).item()
@@ -40,10 +42,7 @@ def predict(image):
     
     return predicted_class
 
-# Detailed Wikipedia info
-from urllib.parse import quote
-import requests
-
+# Function to get detailed Wikipedia info
 def get_detailed_wikipedia_info(query):
     try:
         # Step 1: Search for the article
@@ -80,7 +79,7 @@ def get_detailed_wikipedia_info(query):
 
 # Wrapped predict function
 def wrapped_predict(image):
-    # Step 1: Use your local ML model to predict the disease name
+    # Step 1: Use the model to predict the disease name
     disease_name = predict(image)
     
     # Step 2: Use the disease name to query detailed Wikipedia info