Update app.py

app.py

@@ -3,9 +3,6 @@ import pickle
 import numpy as np
 from tensorflow.keras.models import load_model
 from PIL import Image
-import torch
-import torchvision.transforms as transforms
-import torchvision.models as models
 
 # Update the path to the directory where your models are stored
 model_directory = '/Users/keremoktay/Desktop/Gradio/'
@@ -13,8 +10,8 @@ model_directory = '/Users/keremoktay/Desktop/Gradio/'
 # Load the decision tree and KNN models
 dt_models = {
     "2": pickle.load(open('dt2.pkl', 'rb')),
-    "3": pickle.load(open('dt3.pkl', 'rb')), 
-    "5": pickle.load(open('dt5.pkl', 'rb')) 
+    "3": pickle.load(open('dt3.pkl', 'rb')),
+    "5": pickle.load(open('dt5.pkl', 'rb'))
 }
 knn_models = {
     "1": pickle.load(open('knn1.pkl', 'rb')),
@@ -29,38 +26,27 @@ nn_models = {
     "3": load_model('cnn_3layer.h5')
 }
 
-
-
-model - models.mobilenet_v2()
-model.load_state_dict(torch.load('model_weights.pth'))
-model.eval()
-
 def preprocess_image_for_ml(image_path):
-    image = Image.open(image_path)
-    image_array = np.asarray(image).flatten().reshape(1, -1)
-    return image_array
+    try:
+        # For traditional ML models, just flatten the image without resizing
+        image = Image.open(image_path)
+        image_array = np.asarray(image).flatten().reshape(1, -1)
+        return image_array
+    except Exception as e:
+        print(f"Error in preprocess_image_for_ml: {e}")
+        raise e
 
 def preprocess_image_for_cnn(image_path, target_size=(128, 128)):
-    image = Image.open(image_path).convert('RGB')
-    image = image.resize(target_size)
-    image_array = np.asarray(image) / 255.0
-    image_array = np.expand_dims(image_array, axis=0)
-    return image_array
-
-def preprocess_image_for_pytorch(image_path):
-    preprocess = transforms.compose([
-        transforms.resize(256),
-        transforms.CenterCrop(224),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])
-    ])
-    img = Image.open(image_path)
-    img_t = preprocess(img)
-    batch_t = torch.unsqueeze(img_t, 0)
-    return batch_t
-    
-    
-   
+    try:
+        # For CNN models, convert to RGB, resize to 128x128, normalize, and add a batch dimension
+        image = Image.open(image_path).convert('RGB')
+        image = image.resize(target_size)
+        image_array = np.asarray(image) / 255.0
+        image_array = np.expand_dims(image_array, axis=0)
+        return image_array
+    except Exception as e:
+        print(f"Error in preprocess_image_for_cnn: {e}")
+        raise e
 
 class_names = {
     0: "Angular Leaf Spot",
@@ -69,32 +55,37 @@ class_names = {
 }
 
 def classify_image_with_decision_tree(image, depth):
-    image_array = preprocess_image_for_ml(image)
-    model = dt_models.get(depth)
-    prediction = model.predict(image_array)
-    return class_names[int(prediction)]
+    try:
+        image_array = preprocess_image_for_ml(image)
+        model = dt_models.get(depth)
+        prediction = model.predict(image_array)
+        # Sayısal tahmini hastalık ismine dönüştür
+        return class_names[int(prediction)]
+    except Exception as e:
+        print(f"Error in classify_image_with_decision_tree: {e}")
+        raise e
 
 def classify_image_with_knn(image, k):
-    image_array = preprocess_image_for_ml(image)
-    model = knn_models.get(k)
-    prediction = model.predict(image_array)
-    return class_names[int(prediction)]
+    try:
+        image_array = preprocess_image_for_ml(image)
+        model = knn_models.get(k)
+        prediction = model.predict(image_array)
+        # Sayısal tahmini hastalık ismine dönüştür
+        return class_names[int(prediction)]
+    except Exception as e:
+        print(f"Error in classify_image_with_knn: {e}")
+        raise e
 
 def classify_image_with_neural_network(image, layers):
-    image_array = preprocess_image_for_cnn(image)
-    model = nn_models.get(layers)
-    prediction = model.predict(image_array)
-    return class_names[np.argmax(prediction, axis=1)[0]]
-
-def classify_image_with_pytorch(image):
-    image_tensor = preprocess_image_for_pytorch(image)
-    with torch.no_grad():
-        out = model(image_tensor)
-        _, indices = torch.topk(out, 1)
-        
-        return indices[0]
-
-        
+    try:
+        image_array = preprocess_image_for_cnn(image)
+        model = nn_models.get(layers)
+        prediction = model.predict(image_array)
+        # En yüksek tahmin değerine sahip indeksi bul ve hastalık ismine dönüştür
+        return class_names[np.argmax(prediction, axis=1)[0]]
+    except Exception as e:
+        print(f"Error in classify_image_with_neural_network: {e}")
+        raise e
 
 # Create Gradio interface
 with gr.Blocks() as demo:
@@ -124,11 +115,4 @@ with gr.Blocks() as demo:
         classify_button_nn = gr.Button("Classify with Neural Network")
         classify_button_nn.click(classify_image_with_neural_network, inputs=[image_input_nn, dropdown_layers], outputs=output_nn)
 
-    with gr.Tab("mobilenet Model"):
-        with gr.Row():
-            image_input_pytorch = gr.Image(type="filepath")
-            output_pytorch = gr.Textbox(label="ResNet-18 Model Output")
-        classify_button_pytorch = gr.Button("Classify with ResNet-18 Model")
-        classify_button_pytorch.click(classify_image_with_pytorch, inputs=[image_input_pytorch], outputs=output_pytorch)
-
 demo.launch()