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Gradio.py

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+import gradio as gr
+import pickle
+import numpy as np
+from tensorflow.keras.models import load_model
+from PIL import Image
+
+# Update the path to the directory where your models are stored
+model_directory = '/Users/keremoktay/Desktop/Gradio/'
+
+# Load the decision tree and KNN models
+dt_models = {
+    "2": pickle.load(open(model_directory + 'dt2.pkl', 'rb')),
+    "3": pickle.load(open(model_directory + 'dt3.pkl', 'rb')),
+    "5": pickle.load(open(model_directory + 'dt5.pkl', 'rb'))
+}
+knn_models = {
+    "1": pickle.load(open(model_directory + 'knn1.pkl', 'rb')),
+    "3": pickle.load(open(model_directory + 'knn3.pkl', 'rb')),
+    "5": pickle.load(open(model_directory + 'knn5.pkl', 'rb'))
+}
+
+# Load the neural network models
+nn_models = {
+    "1": load_model(model_directory + 'cnn_1layer.h5'),
+    "2": load_model(model_directory + 'cnn_2layer.h5'),
+    "3": load_model(model_directory + 'cnn_3layer.h5')
+}
+
+def preprocess_image_for_ml(image_path):
+    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)):
+    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",
+    1: "Bean Rust",
+    2: "Healthy"
+}
+
+def classify_image_with_decision_tree(image, depth):
+    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):
+    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):
+    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:
+    gr.Markdown("Image Classification using Different Models")
+    
+    with gr.Tab("Decision Tree"):
+        with gr.Row():
+            image_input_dt = gr.Image(type="filepath")
+            dropdown_depth = gr.Dropdown(label="Select Depth", choices=["2", "3", "5"])
+            output_dt = gr.Textbox(label="Decision Tree Output")
+        classify_button_dt = gr.Button("Classify with Decision Tree")
+        classify_button_dt.click(classify_image_with_decision_tree, inputs=[image_input_dt, dropdown_depth], outputs=output_dt)
+
+    with gr.Tab("K-Nearest Neighbors (KNN)"):
+        with gr.Row():
+            image_input_knn = gr.Image(type="filepath")
+            dropdown_k = gr.Dropdown(label="Select Number of Neighbors (k)", choices=["1", "3", "5"])
+            output_knn = gr.Textbox(label="KNN Output")
+        classify_button_knn = gr.Button("Classify with KNN")
+        classify_button_knn.click(classify_image_with_knn, inputs=[image_input_knn, dropdown_k], outputs=output_knn)
+
+    with gr.Tab("Neural Network"):
+        with gr.Row():
+            image_input_nn = gr.Image(type="filepath")
+            dropdown_layers = gr.Dropdown(label="Select Number of Layers", choices=["1", "2", "3"])
+            output_nn = gr.Textbox(label="Neural Network Output")
+        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)
+
+demo.launch(share=True)

cnn_1layer.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f2831e5f6636a69d91b96a12afb011cb56da79cdab79114fd27037153c954616
+size 390215648

cnn_2layer.h5

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+oid sha256:6d0708e873f5de90b2e262ab603f6d7ed5bba0097736e63560a418a233a674cd
+size 183170752

cnn_3layer.h5

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+oid sha256:9bb1d8d52ce7d6039e7e0665c2c382e3971348255d760d77c0c7ff9581e519ca
+size 39262856

dt2.pkl

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+oid sha256:1f56fc087c0691a196b1d11ff680326c538b793e2c6ca9fe24a633da5b74974a
+size 1744

dt3.pkl

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+oid sha256:4faed4214fb0167d1c2bd364fa5fa2f5aa28ccbb4a3a773242c036d994aef870
+size 2451

dt5.pkl

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+oid sha256:de9cfa01e769120d2607d8c0c3ccb6f0d01ba0a7f3a10128a9b4dc4660fdc1f2
+size 5971

knn1.pkl

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+size 775508987

knn3.pkl

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+size 775508987

knn5.pkl

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