Upload 3 files

.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
+SoilNet.keras filter=lfs diff=lfs merge=lfs -text

SoilNet.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f65cd8fdae31632ff7a767ed5c23dc76b848dd57fcc9c0f4f54fb41eda790c3
+size 134081172

app.py

@@ -0,0 +1,115 @@
+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)

requirements.txt

@@ -0,0 +1,7 @@
+werkzeug==1.0.0
+numpy==1.19.1
+gunicorn==19.9.0
+Flask==1.1.1
+h5py==2.9.0
+tensorflow==2.3.0
+pillow>=6.2.0