Initial commit for Tomato Disease Detection Space

.gitattributes

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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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README.md

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----
-title: Tomato Disease Training
-emoji: 🐢
-colorFrom: pink
-colorTo: pink
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Tomato Disease Training
+emoji: 🐢
+colorFrom: pink
+colorTo: pink
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

hf_app.py

@@ -1,109 +1,109 @@
-import os
-import cv2
-import numpy as np
-import torch
-import gradio as gr
-from fastapi import FastAPI, File, UploadFile
-from sklearn.preprocessing import StandardScaler
-from sklearn.svm import SVC
-
-# Список классов болезней
-DISEASE_CLASSES = [
-    'Tomato___Bacterial_spot', 
-    'Tomato___Early_blight', 
-    'Tomato___Late_blight', 
-    'Tomato___Leaf_Mold', 
-    'Tomato___Septoria_leaf_spot', 
-    'Tomato___Spider_mites Two-spotted_spider_mite', 
-    'Tomato___Target_Spot', 
-    'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 
-    'Tomato___Tomato_mosaic_virus',
-    'Tomato___healthy'
-]
-
-def preprocess_image(image):
-    """Подготовка изображения для предсказания"""
-    if image is None:
-        return None
-    
-    # Resize и flatten
-    img_resized = cv2.resize(image, (64, 64))
-    img_flattened = img_resized.flatten()
-    
-    return img_flattened
-
-def load_model():
-    """Загрузка обученной модели"""
-    try:
-        # Загрузка модели SVM
-        model_path = '/tmp/data/state/SVC_comb_R.pth.pth'
-        
-        # Если модель не существует, возвращаем None
-        if not os.path.exists(model_path):
-            print(f"Модель не найдена по пути: {model_path}")
-            return None, None
-        
-        model_data = torch.load(model_path)
-        
-        # Создание pipeline с масштабированием
-        scaler = StandardScaler()
-        scaler.mean_ = model_data['mean']
-        scaler.scale_ = model_data['std']
-        
-        classifier = model_data['classifier']
-        
-        return scaler, classifier
-    except Exception as e:
-        print(f"Ошибка загрузки модели: {e}")
-        return None, None
-
-def predict_disease(image):
-    """Предсказание болезни томата"""
-    if image is None:
-        return "Пожалуйста, загрузите изображение"
-    
-    # Загрузка модели
-    scaler, classifier = load_model()
-    if scaler is None or classifier is None:
-        return "Ошибка загрузки модели. Возможно, нужно сначала обучить модель."
-    
-    # Предобработка изображения
-    processed_image = preprocess_image(image)
-    if processed_image is None:
-        return "Не удалось обработать изображение"
-    
-    # Масштабирование
-    processed_image = scaler.transform([processed_image])
-    
-    # Предсказание
-    prediction = classifier.predict(processed_image)
-    probabilities = classifier.predict_proba(processed_image)[0]
-    
-    # Формирование результата
-    result = f"Обнаружено: {prediction[0]}\n\n"
-    result += "Вероятности:\n"
-    for disease, prob in zip(DISEASE_CLASSES, probabilities):
-        result += f"{disease}: {prob*100:.2f}%\n"
-    
-    return result
-
-# FastAPI приложение
-app = FastAPI()
-
-# Gradio интерфейс
-iface = gr.Interface(
-    fn=predict_disease,
-    inputs=gr.Image(type="numpy", label="Загрузите изображение листа томата"),
-    outputs=gr.Textbox(label="Результат диагностики"),
-    title="Диагностика болезней томатов",
-    description="Загрузите изображение листа томата для определения заболевания"
-)
-
-# Маршрут для Gradio
-@app.get("/")
-def read_root():
-    return {"status": "Tomato Disease Classifier is running"}
-
-# Запуск Gradio
-if __name__ == "__main__":
-    iface.launch(server_name="0.0.0.0", server_port=7860)
+import os
+import cv2
+import numpy as np
+import torch
+import gradio as gr
+from fastapi import FastAPI, File, UploadFile
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVC
+
+# Список классов болезней
+DISEASE_CLASSES = [
+    'Tomato___Bacterial_spot', 
+    'Tomato___Early_blight', 
+    'Tomato___Late_blight', 
+    'Tomato___Leaf_Mold', 
+    'Tomato___Septoria_leaf_spot', 
+    'Tomato___Spider_mites Two-spotted_spider_mite', 
+    'Tomato___Target_Spot', 
+    'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 
+    'Tomato___Tomato_mosaic_virus',
+    'Tomato___healthy'
+]
+
+def preprocess_image(image):
+    """Подготовка изображения для предсказания"""
+    if image is None:
+        return None
+    
+    # Resize и flatten
+    img_resized = cv2.resize(image, (64, 64))
+    img_flattened = img_resized.flatten()
+    
+    return img_flattened
+
+def load_model():
+    """Загрузка обученной модели"""
+    try:
+        # Загрузка модели SVM
+        model_path = '/tmp/data/state/SVC_comb_R.pth.pth'
+        
+        # Если модель не существует, возвращаем None
+        if not os.path.exists(model_path):
+            print(f"Модель не найдена по пути: {model_path}")
+            return None, None
+        
+        model_data = torch.load(model_path)
+        
+        # Создание pipeline с масштабированием
+        scaler = StandardScaler()
+        scaler.mean_ = model_data['mean']
+        scaler.scale_ = model_data['std']
+        
+        classifier = model_data['classifier']
+        
+        return scaler, classifier
+    except Exception as e:
+        print(f"Ошибка загрузки модели: {e}")
+        return None, None
+
+def predict_disease(image):
+    """Предсказание болезни томата"""
+    if image is None:
+        return "Пожалуйста, загрузите изображение"
+    
+    # Загрузка модели
+    scaler, classifier = load_model()
+    if scaler is None or classifier is None:
+        return "Ошибка загрузки модели. Возможно, нужно сначала обучить модель."
+    
+    # Предобработка изображения
+    processed_image = preprocess_image(image)
+    if processed_image is None:
+        return "Не удалось обработать изображение"
+    
+    # Масштабирование
+    processed_image = scaler.transform([processed_image])
+    
+    # Предсказание
+    prediction = classifier.predict(processed_image)
+    probabilities = classifier.predict_proba(processed_image)[0]
+    
+    # Формирование результата
+    result = f"Обнаружено: {prediction[0]}\n\n"
+    result += "Вероятности:\n"
+    for disease, prob in zip(DISEASE_CLASSES, probabilities):
+        result += f"{disease}: {prob*100:.2f}%\n"
+    
+    return result
+
+# FastAPI приложение
+app = FastAPI()
+
+# Gradio интерфейс
+iface = gr.Interface(
+    fn=predict_disease,
+    inputs=gr.Image(type="numpy", label="Загрузите изображение листа томата"),
+    outputs=gr.Textbox(label="Результат диагностики"),
+    title="Диагностика болезней томатов",
+    description="Загрузите изображение листа томата для определения заболевания"
+)
+
+# Маршрут для Gradio
+@app.get("/")
+def read_root():
+    return {"status": "Tomato Disease Classifier is running"}
+
+# Запуск Gradio
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)