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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+plant_disease_detection_saved_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,43 @@
+from flask import Flask, request, jsonify
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import io
+import json
+
+app = Flask(__name__)
+
+# Load the TensorFlow model
+model = tf.keras.models.load_model('./plant_disease_detection_saved_model')
+
+# Load categories
+with open('./categories.json') as f:
+    categories = json.load(f)
+
+def preprocess_image(image):
+    # Convert the image to a NumPy array
+    image = Image.open(io.BytesIO(image))
+    image = image.resize((224, 224))  # Adjust size as needed
+    image_array = np.array(image) / 255.0  # Normalize to [0, 1]
+    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+    return image_array
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'image' not in request.files:
+        return jsonify({'error': 'No image provided'}), 400
+
+    image = request.files['image'].read()
+    image_array = preprocess_image(image)
+
+    # Make prediction
+    predictions = model.predict(image_array)
+    predicted_class = np.argmax(predictions, axis=1)[0]
+
+    # Map to category names
+    predicted_label = categories.get(str(predicted_class), 'Unknown')
+
+    return jsonify({'class': predicted_label, 'confidence': float(predictions[0][predicted_class])})
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=8080, debug=True)

categories.json

@@ -0,0 +1,40 @@
+{
+    "0": "Apple___Apple_scab",
+    "1": "Apple___Black_rot",
+    "2": "Apple___Cedar_apple_rust",
+    "3": "Apple___healthy",
+    "4": "Blueberry___healthy",
+    "5": "Cherry_(including_sour)___Powdery_mildew",
+    "6": "Cherry_(including_sour)___healthy",
+    "7": "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot",
+    "8": "Corn_(maize)___Common_rust_",
+    "9": "Corn_(maize)___Northern_Leaf_Blight",
+    "10": "Corn_(maize)___healthy",
+    "11": "Grape___Black_rot",
+    "12": "Grape___Esca_(Black_Measles)",
+    "13": "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)",
+    "14": "Grape___healthy",
+    "15": "Orange___Haunglongbing_(Citrus_greening)",
+    "16": "Peach___Bacterial_spot",
+    "17": "Peach___healthy",
+    "18": "Pepper,_bell___Bacterial_spot",
+    "19": "Pepper,_bell___healthy",
+    "20": "Potato___Early_blight",
+    "21": "Potato___Late_blight",
+    "22": "Potato___healthy",
+    "23": "Raspberry___healthy",
+    "24": "Soybean___healthy",
+    "25": "Squash___Powdery_mildew",
+    "26": "Strawberry___Leaf_scorch",
+    "27": "Strawberry___healthy",
+    "28": "Tomato___Bacterial_spot",
+    "29": "Tomato___Early_blight",
+    "30": "Tomato___Late_blight",
+    "31": "Tomato___Leaf_Mold",
+    "32": "Tomato___Septoria_leaf_spot",
+    "33": "Tomato___Spider_mites Two-spotted_spider_mite",
+    "34": "Tomato___Target_Spot",
+    "35": "Tomato___Tomato_Yellow_Leaf_Curl_Virus",
+    "36": "Tomato___Tomato_mosaic_virus",
+    "37": "Tomato___healthy"
+}

plant_disease_detection.h5

@@ -0,0 +1,3 @@
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plant_disease_detection_saved_model/fingerprint.pb

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plant_disease_detection_saved_model/keras_metadata.pb

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plant_disease_detection_saved_model/saved_model.pb

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plant_disease_detection_saved_model/variables/variables.data-00000-of-00001

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plant_disease_detection_v2_17.h5

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