Create app.py

app.py

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+import numpy as np
+import cv2
+import glob
+import os
+import matplotlib.pyplot as plt
+import string
+from mlxtend.plotting import plot_decision_regions
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import StandardScaler
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split, cross_val_score
+from sklearn.utils.multiclass import unique_labels
+from sklearn import metrics
+from sklearn.svm import SVC
+dim = 100
+import torch
+from torchvision import transforms
+from PIL import Image
+
+# Define your model class
+class YourModelClass(torch.nn.Module):
+    # Define your model architecture here
+
+# Create an instance of your model
+model = YourModelClass()
+
+# Load the pre-trained weights
+model.load_state_dict(torch.load('model_weights.pth'))
+model.eval()
+def predict_leaf_health(image_path):
+    try:
+        # Open and preprocess the image
+        img = Image.open(image_path)
+        transform = transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ])
+        img = transform(img)
+        img = img.unsqueeze(0)  # Add batch dimension
+
+        # Make prediction
+        with torch.no_grad():
+            output = model(img)
+            prediction = torch.argmax(output).item()
+
+        # Map the prediction to class labels (modify as needed)
+        class_labels = {0: 'Unhealthy', 1: 'Healthy'}
+        result = class_labels.get(prediction, 'Unknown')
+
+        return result
+
+    except Exception as e:
+        return f"Error: {str(e)}"