File size: 3,866 Bytes
171301a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img, img_to_array
import numpy as np
from flask import Flask, request, render_template, jsonify
from werkzeug.utils import secure_filename
import os
# Initialize Flask app
app = Flask(__name__)
# Load model
model_path = os.path.join(os.path.dirname(__file__), "SoilNet.keras")
SoilNet = load_model(model_path)
# Classes dictionary
classes = {
0: "Alluvial Soil:-{ Rice, Wheat, Sugarcane, Maize, Cotton, Soyabean, Jute }",
1: "Black Soil:-{ Virginia, Wheat, Jowar, Millets, Linseed, Castor, Sunflower }",
2: "Clay Soil:-{ Rice, Lettuce, Chard, Broccoli, Cabbage, Snap Beans }",
3: "Red Soil:-{ Cotton, Wheat, Pulses, Millets, Oil Seeds, Potatoes }"
}
# API Key (set this securely in prod)
API_KEY = "your-secret-api-key-1234"
# Prediction function
def model_predict(image_path, model):
image = load_img(image_path, target_size=(224, 224))
image = img_to_array(image) / 255.0
image = np.expand_dims(image, axis=0)
result = np.argmax(model.predict(image), axis=-1)[0]
prediction = classes[result]
if result == 0:
return "Alluvial", "Alluvial.html"
elif result == 1:
return "Black", "Black.html"
elif result == 2:
return "Clay", "Clay.html"
elif result == 3:
return "Red", "Red.html"
# Route: Home (form)
@app.route('/', methods=['GET'])
def index():
return render_template('index.html')
# Route: Form-based upload + result display
@app.route('/predict', methods=['POST'])
def predict():
file = request.files.get('image')
if not file or file.filename == '':
return "No image uploaded", 400
# Validate extension
filename = secure_filename(file.filename)
if not filename.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.gif')):
return "Unsupported file type", 400
# Save image
upload_folder = os.path.join(os.path.dirname(__file__), 'static', 'user_uploaded')
os.makedirs(upload_folder, exist_ok=True)
file_path = os.path.join(upload_folder, filename)
file.save(file_path)
# Check image is valid
try:
_ = load_img(file_path)
except Exception as e:
os.remove(file_path)
return f"Invalid image file: {e}", 400
pred, output_page = model_predict(file_path, SoilNet)
user_image_path = os.path.join('static', 'user_uploaded', filename)
return render_template(output_page, pred_output=pred, user_image=user_image_path)
# Route: API endpoint with API key
@app.route('/api/predict', methods=['POST'])
def api_predict():
# Check for API key
key = request.headers.get('x-api-key')
if key != API_KEY:
return jsonify({"error": "Unauthorized"}), 401
# Validate image
file = request.files.get('image')
if not file or file.filename == '':
return jsonify({"error": "No image uploaded"}), 400
filename = secure_filename(file.filename)
if not filename.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.gif')):
return jsonify({"error": "Unsupported file type"}), 400
# Save temp image
api_temp_folder = os.path.join(os.path.dirname(__file__), 'static', 'api_temp')
os.makedirs(api_temp_folder, exist_ok=True)
file_path = os.path.join(api_temp_folder, filename)
file.save(file_path)
try:
_ = load_img(file_path)
except Exception as e:
os.remove(file_path)
return jsonify({"error": f"Invalid image: {str(e)}"}), 400
pred, _ = model_predict(file_path, SoilNet)
os.remove(file_path) # Optional: delete after prediction
return jsonify({"soil_type": pred})
# Start the app
if __name__ == '__main__':
app.run(debug=True, threaded=False)
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