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"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "3ba22d8d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Current directory: C:\\Users\\HP\\NitroSense-AI\n",
"Target file: C:\\Users\\HP\\NitroSense-AI\\app.py\n",
"✅ app.py created successfully with session check and flash messages!\n",
"File location: C:\\Users\\HP\\NitroSense-AI\\app.py\n",
"✅ Verified: app.py exists (20.96 KB)\n"
]
}
],
"source": [
"# %% [markdown]\n",
"# # Create app.py file\n",
"# ## Run this to generate the Flask application file\n",
"\n",
"# %%\n",
"import os\n",
"\n",
"# Get current directory\n",
"current_dir = os.getcwd()\n",
"print(f\"Current directory: {current_dir}\")\n",
"print(f\"Target file: {os.path.join(current_dir, 'app.py')}\")\n",
"\n",
"# %%\n",
"# Complete Flask app code with session check and flash messages\n",
"app_code = '''import os\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow.keras import backend as K\n",
"from flask import Flask, render_template, request, jsonify, session, redirect, url_for, flash\n",
"from werkzeug.utils import secure_filename\n",
"from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
"import cv2\n",
"from datetime import datetime\n",
"import pickle\n",
"import uuid\n",
"import time\n",
"import shutil\n",
"from PIL import Image\n",
"import traceback\n",
"import sys\n",
"\n",
"# Import utilities\n",
"from utils.background_removal import BackgroundRemover\n",
"from utils.edge_detection import EdgeDetector\n",
"from utils.preprocessing import ImagePreprocessor\n",
"\n",
"# ============= DEFINE CUSTOM ACTIVATION FUNCTIONS =============\n",
"def mish_activation(x):\n",
" \"\"\"\n",
" Mish activation function: x * tanh(softplus(x))\n",
" \"\"\"\n",
" return x * tf.math.tanh(tf.math.softplus(x))\n",
"\n",
"def swish_activation(x):\n",
" \"\"\"\n",
" Swish activation function: x * sigmoid(x)\n",
" \"\"\"\n",
" return x * tf.nn.sigmoid(x)\n",
"\n",
"# Register custom activations\n",
"tf.keras.utils.get_custom_objects()['mish_activation'] = mish_activation\n",
"tf.keras.utils.get_custom_objects()['swish_activation'] = swish_activation\n",
"# ==============================================================\n",
"\n",
"# Define custom metrics functions (same as during training)\n",
"def rmse(y_true, y_pred):\n",
" \"\"\"Root Mean Square Error\"\"\"\n",
" return K.sqrt(K.mean(K.square(y_pred - y_true)))\n",
"\n",
"def r2(y_true, y_pred):\n",
" \"\"\"R-squared (Coefficient of determination)\"\"\"\n",
" SS_res = K.sum(K.square(y_true - y_pred))\n",
" SS_tot = K.sum(K.square(y_true - K.mean(y_true)))\n",
" return 1 - SS_res/(SS_tot + K.epsilon())\n",
"\n",
"def mae(y_true, y_pred):\n",
" \"\"\"Mean Absolute Error\"\"\"\n",
" return K.mean(K.abs(y_pred - y_true))\n",
"\n",
"# Create Flask app\n",
"app = Flask(__name__)\n",
"app.secret_key = 'nitrosense-secret-key-2024'\n",
"app.config['SESSION_TYPE'] = 'filesystem'\n",
"app.config['SESSION_PERMANENT'] = False\n",
"app.config['SESSION_USE_SIGNER'] = True\n",
"app.config['SESSION_COOKIE_NAME'] = 'nitrosense_session'\n",
"app.config['SESSION_COOKIE_SECURE'] = False\n",
"app.config['SESSION_COOKIE_HTTPONLY'] = True\n",
"app.config['SESSION_COOKIE_SAMESITE'] = 'Lax'\n",
"\n",
"# Configuration\n",
"app.config['UPLOAD_FOLDER'] = 'static/uploads'\n",
"app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024\n",
"app.config['ALLOWED_EXTENSIONS'] = {'png', 'jpg', 'jpeg', 'gif'}\n",
"\n",
"# Initialize models and utilities\n",
"print(\"=\"*50)\n",
"print(\"Loading NitroSense AI Application\")\n",
"print(\"=\"*50)\n",
"\n",
"# ============= LOAD SOIL TEMPERATURE MEAN =============\n",
"print(\"\\\\n📊 Loading soil temperature mean from training data...\")\n",
"try:\n",
" with open('utils/temperature_means.pkl', 'rb') as f:\n",
" temp_means = pickle.load(f)\n",
" SOIL_TEMP_MEAN = temp_means.get('soil_temp_mean', 29.8)\n",
" print(f\"✅ Using Soil Temperature mean: {SOIL_TEMP_MEAN}°C\")\n",
" print(f\"ℹ️ Air Temperature will be provided by user input\")\n",
"except Exception as e:\n",
" print(f\"⚠️ Could not load temperature means: {e}\")\n",
" print(\" Using default soil temperature:\")\n",
" SOIL_TEMP_MEAN = 29.8\n",
" print(f\" Soil Temp: {SOIL_TEMP_MEAN}°C\")\n",
"# =====================================================\n",
"\n",
"# Load U2Net model (portable version)\n",
"print(\"\\\\n📦 Initializing Background Remover...\")\n",
"try:\n",
" background_remover = BackgroundRemover()\n",
" print(\"✅ BackgroundRemover initialized (portable version)\")\n",
" print(\" Weights should be at: u2net/weights/u2net.pth\")\n",
"except Exception as e:\n",
" print(f\"⚠️ Error initializing BackgroundRemover: {e}\")\n",
" print(\" Will use fallback mode (no background removal)\")\n",
" background_remover = None\n",
"\n",
"# Load edge detector\n",
"print(\"\\\\n📦 Initializing Edge Detector...\")\n",
"try:\n",
" edge_detector = EdgeDetector()\n",
" print(\"✅ EdgeDetector initialized\")\n",
"except Exception as e:\n",
" print(f\"⚠️ Error initializing EdgeDetector: {e}\")\n",
" edge_detector = None\n",
"\n",
"# Load preprocessor - Use 224x224 to match DenseNet121 model\n",
"print(\"\\\\n📦 Initializing Image Preprocessor...\")\n",
"try:\n",
" preprocessor = ImagePreprocessor(target_size=(224, 224))\n",
" print(\"✅ ImagePreprocessor initialized with target_size=(224, 224)\")\n",
"except Exception as e:\n",
" print(f\"⚠️ Error initializing ImagePreprocessor: {e}\")\n",
" preprocessor = None\n",
"\n",
"# Load your trained model with custom metrics\n",
"print(\"\\\\n📦 Loading Model...\")\n",
"model_path = os.path.join('models', 'Pyramid_fusion_densenet121_model.h5')\n",
"\n",
"if os.path.exists(model_path):\n",
" try:\n",
" custom_objects = {\n",
" 'mse': tf.keras.losses.MeanSquaredError(),\n",
" 'MSE': tf.keras.losses.MeanSquaredError(),\n",
" 'mean_squared_error': tf.keras.losses.MeanSquaredError(),\n",
" 'mae': mae,\n",
" 'MAE': mae,\n",
" 'mean_absolute_error': mae,\n",
" 'rmse': rmse,\n",
" 'RMSE': rmse,\n",
" 'root_mean_squared_error': rmse,\n",
" 'r2': r2,\n",
" 'R2': r2,\n",
" 'r_squared': r2,\n",
" 'R_squared': r2,\n",
" 'mish_activation': mish_activation,\n",
" 'swish_activation': swish_activation,\n",
" }\n",
" \n",
" model = tf.keras.models.load_model(\n",
" model_path, \n",
" custom_objects=custom_objects,\n",
" compile=False\n",
" )\n",
" print(f\"✅ Model loaded successfully from: {model_path}\")\n",
" \n",
" print(\"\\\\n📊 Model input structure:\")\n",
" for i, input_layer in enumerate(model.inputs):\n",
" print(f\" Input {i+1}: {input_layer.name} - Shape: {input_layer.shape}\")\n",
" \n",
" model.compile(\n",
" optimizer='adam',\n",
" loss='mse',\n",
" metrics=[mae, rmse, r2]\n",
" )\n",
" print(\"✅ Model recompiled with custom metrics\")\n",
" \n",
" except Exception as e:\n",
" print(f\"⚠️ Error loading model: {e}\")\n",
" traceback.print_exc()\n",
" model = None\n",
"else:\n",
" print(f\"❌ Model file not found at: {model_path}\")\n",
"\n",
"# Load all scalers\n",
"print(\"\\\\n📦 Loading All Scalers...\")\n",
"try:\n",
" continuous_scaler_path = 'utils/scalers/continuous_scaler.pkl'\n",
" if os.path.exists(continuous_scaler_path):\n",
" continuous_scaler = pickle.load(open(continuous_scaler_path, 'rb'))\n",
" print(\"✅ continuous_scaler loaded successfully\")\n",
" else:\n",
" continuous_scaler = None\n",
" print(\"⚠️ continuous_scaler not found\")\n",
" \n",
" days_scaler_path = 'utils/scalers/days_scaler.pkl'\n",
" if os.path.exists(days_scaler_path):\n",
" days_scaler = pickle.load(open(days_scaler_path, 'rb'))\n",
" print(\"✅ days_scaler loaded successfully\")\n",
" else:\n",
" days_scaler = None\n",
" print(\"⚠️ days_scaler not found\")\n",
" \n",
" nitrogen_map_path = 'utils/scalers/nitrogen_map.pkl'\n",
" if os.path.exists(nitrogen_map_path):\n",
" nitrogen_map = pickle.load(open(nitrogen_map_path, 'rb'))\n",
" print(\"✅ nitrogen_map loaded successfully\")\n",
" else:\n",
" nitrogen_map = {0:0, 30:1, 60:2, 90:3, 120:4, 150:5, 180:6, 210:7}\n",
" print(\"⚠️ nitrogen_map not found, using default mapping\")\n",
" \n",
" scaler_y_path = 'utils/scalers/scaler_y.pkl'\n",
" if os.path.exists(scaler_y_path):\n",
" scaler_y = pickle.load(open(scaler_y_path, 'rb'))\n",
" print(f\"✅ scaler_y loaded successfully\")\n",
" print(f\" Target scaler mean: {scaler_y.mean_[0]:.4f}\")\n",
" print(f\" Target scaler scale: {scaler_y.scale_[0]:.4f}\")\n",
" else:\n",
" scaler_y = None\n",
" print(\"⚠️ scaler_y not found\")\n",
" \n",
"except Exception as e:\n",
" print(f\"⚠️ Error loading scalers: {e}\")\n",
" continuous_scaler = None\n",
" days_scaler = None\n",
" nitrogen_map = {0:0, 30:1, 60:2, 90:3, 120:4, 150:5, 180:6, 210:7}\n",
" scaler_y = None\n",
"\n",
"print(\"\\\\n\" + \"=\"*50)\n",
"print(\"✅ Application initialization complete!\")\n",
"print(\"=\"*50)\n",
"\n",
"def cleanup_old_images(max_age_hours=24):\n",
" \"\"\"Delete images older than max_age_hours\"\"\"\n",
" try:\n",
" upload_folder = app.config['UPLOAD_FOLDER']\n",
" if not os.path.exists(upload_folder):\n",
" return\n",
" current_time = time.time()\n",
" for filename in os.listdir(upload_folder):\n",
" filepath = os.path.join(upload_folder, filename)\n",
" if os.path.isfile(filepath):\n",
" file_age = current_time - os.path.getctime(filepath)\n",
" if file_age > max_age_hours * 3600:\n",
" os.remove(filepath)\n",
" print(f\"🧹 Deleted old image: {filename}\")\n",
" except Exception as e:\n",
" print(f\"⚠️ Error cleaning up images: {e}\")\n",
"\n",
"def allowed_file(filename):\n",
" return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['ALLOWED_EXTENSIONS']\n",
"\n",
"def calculate_days(sowing_date, capture_date):\n",
" \"\"\"Calculate number of days between sowing and capture\"\"\"\n",
" date_format = \"%Y-%m-%d\"\n",
" sowing = datetime.strptime(sowing_date, date_format)\n",
" capture = datetime.strptime(capture_date, date_format)\n",
" days = (capture - sowing).days\n",
" return max(days, 0)\n",
"\n",
"def map_fertilizer_to_category(fertilizer_value):\n",
" \"\"\"Map continuous fertilizer value (0-210) to categorical bin (0-7)\"\"\"\n",
" nitrogen_bins = [0, 30, 60, 90, 120, 150, 180, 210]\n",
" nitrogen_map = {0:0, 30:1, 60:2, 90:3, 120:4, 150:5, 180:6, 210:7}\n",
" closest_bin = min(nitrogen_bins, key=lambda x: abs(x - fertilizer_value))\n",
" return nitrogen_map[closest_bin]\n",
"\n",
"def classify_nitrogen_level(nitrogen_value):\n",
" \"\"\"Classify nitrogen content into categories\"\"\"\n",
" if nitrogen_value < 3.0:\n",
" return {\n",
" 'category': 'Deficient',\n",
" 'message': '⚠️ Nitrogen Deficient - Fertilizer Recommended',\n",
" 'color': 'warning',\n",
" 'action': 'Apply nitrogen fertilizer'\n",
" }\n",
" elif nitrogen_value <= 4.0:\n",
" return {\n",
" 'category': 'Sufficient',\n",
" 'message': '✅ Nitrogen Sufficient - No Fertilizer Needed',\n",
" 'color': 'success',\n",
" 'action': 'Maintain current practices'\n",
" }\n",
" else:\n",
" return {\n",
" 'category': 'Excess',\n",
" 'message': '⚠️ Nitrogen Excess - Reduce Fertilizer Application',\n",
" 'color': 'danger',\n",
" 'action': 'Reduce or skip nitrogen application'\n",
" }\n",
"\n",
"@app.route('/')\n",
"def index():\n",
" return render_template('index.html')\n",
" \n",
"@app.route('/estimation')\n",
"def estimation():\n",
" \"\"\"Estimation page route - redirects to home if accessed directly without proper session\"\"\"\n",
" # Check URL parameters for image filename (from capture page)\n",
" image_param = request.args.get('image')\n",
" \n",
" if image_param:\n",
" # If coming from capture page with image parameter\n",
" session['current_image'] = image_param\n",
" session.modified = True\n",
" return render_template('estimation.html')\n",
" \n",
" # Check if there's an image in session (uploaded from file or capture)\n",
" if 'current_image' not in session:\n",
" # This appears to be a direct access - redirect to home page\n",
" flash('Please upload or capture a wheat leaf image first before estimation.', 'info')\n",
" return redirect(url_for('index'))\n",
" \n",
" return render_template('estimation.html')\n",
" \n",
"@app.route('/capture')\n",
"def capture():\n",
" return render_template('capture.html')\n",
"\n",
"@app.route('/upload', methods=['POST'])\n",
"def upload_file():\n",
" try:\n",
" if 'image' not in request.files:\n",
" return jsonify({'error': 'No image uploaded'}), 400\n",
" \n",
" file = request.files['image']\n",
" print(f\"📤 Upload - File received: {file.filename}\")\n",
" \n",
" if file.filename == '':\n",
" return jsonify({'error': 'No image selected'}), 400\n",
" \n",
" if file and allowed_file(file.filename):\n",
" os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)\n",
" \n",
" filename = str(uuid.uuid4()) + '_' + secure_filename(file.filename)\n",
" filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n",
" file.save(filepath)\n",
" print(f\"💾 File saved to: {filepath}\")\n",
" \n",
" if os.path.exists(filepath) and os.path.getsize(filepath) > 0:\n",
" session['current_image'] = filename\n",
" session.modified = True\n",
" \n",
" return jsonify({\n",
" 'success': True,\n",
" 'filename': filename,\n",
" 'image_url': f'/static/uploads/{filename}'\n",
" })\n",
" else:\n",
" return jsonify({'error': 'Failed to save file'}), 400\n",
" \n",
" return jsonify({'error': 'Invalid file type'}), 400\n",
" \n",
" except Exception as e:\n",
" print(f\"❌ Upload error: {e}\")\n",
" traceback.print_exc()\n",
" return jsonify({'error': str(e)}), 500\n",
"\n",
"@app.route('/predict', methods=['POST'])\n",
"def predict():\n",
" cleanup_old_images(24)\n",
" \n",
" try:\n",
" if request.is_json:\n",
" data = request.get_json()\n",
" else:\n",
" data = request.form\n",
" \n",
" print(f\"📨 Form data received: {data}\")\n",
" \n",
" # Get continuous fertilizer value\n",
" try:\n",
" fertilizer_amount = float(data.get('fertilizer'))\n",
" if fertilizer_amount < 0 or fertilizer_amount > 210:\n",
" return jsonify({'error': 'Fertilizer amount must be between 0 and 210 kg/ha'}), 400\n",
" except (TypeError, ValueError):\n",
" return jsonify({'error': 'Please provide a valid fertilizer amount between 0-210 kg/ha'}), 400\n",
" \n",
" # Get dates and calculate days\n",
" sowing_date = data.get('sowingDate')\n",
" capture_date = data.get('captureDate')\n",
" days = calculate_days(sowing_date, capture_date)\n",
" \n",
" if days < 60 or days > 120:\n",
" return jsonify({'error': f'Days must be between 60-120. Current: {days}'}), 400\n",
" \n",
" # Get air temperature\n",
" try:\n",
" air_temp = float(data.get('airTemp'))\n",
" except (TypeError, ValueError):\n",
" return jsonify({'error': 'Please provide valid air temperature value'}), 400\n",
" \n",
" soil_temp = SOIL_TEMP_MEAN\n",
" \n",
" print(f\"\\\\n📝 Input parameters:\")\n",
" print(f\" Fertilizer: {fertilizer_amount} kg/ha\")\n",
" print(f\" Days after sowing: {days}\")\n",
" print(f\" Air Temperature: {air_temp}°C\")\n",
" print(f\" Soil Temperature: {soil_temp}°C (mean from training)\")\n",
" \n",
" nitrogen_category = map_fertilizer_to_category(fertilizer_amount)\n",
" print(f\" Fertilizer {fertilizer_amount} kg/ha → Category {nitrogen_category}\")\n",
" \n",
" if any(v is None for v in [continuous_scaler, days_scaler, scaler_y]):\n",
" missing = []\n",
" if continuous_scaler is None: missing.append(\"continuous_scaler\")\n",
" if days_scaler is None: missing.append(\"days_scaler\")\n",
" if scaler_y is None: missing.append(\"scaler_y\")\n",
" return jsonify({'error': f'Scalers not loaded: {\", \".join(missing)}'}), 500\n",
" \n",
" # Get image from session\n",
" filename = session.get('current_image')\n",
" if not filename:\n",
" return jsonify({'error': 'No image found. Please upload an image first.'}), 400\n",
" \n",
" image_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n",
" \n",
" if not os.path.exists(image_path):\n",
" return jsonify({'error': f'Image file not found'}), 400\n",
" \n",
" # Read image\n",
" image = cv2.imread(image_path)\n",
" if image is None:\n",
" try:\n",
" pil_img = Image.open(image_path)\n",
" if pil_img.mode != 'RGB':\n",
" pil_img = pil_img.convert('RGB')\n",
" image = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)\n",
" print(f\"✅ PIL fallback succeeded\")\n",
" except Exception as e:\n",
" print(f\"❌ All image reading methods failed: {e}\")\n",
" return jsonify({'error': 'Could not read image'}), 400\n",
" \n",
" image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
" \n",
" # Background removal\n",
" if background_remover is not None:\n",
" try:\n",
" bg_removed, mask = background_remover.remove_background(\n",
" image_rgb, target_size=(224, 224), max_size=800\n",
" )\n",
" print(f\"✅ Background removal completed\")\n",
" except Exception as e:\n",
" print(f\"⚠️ Background removal error: {e}\")\n",
" bg_removed = cv2.resize(image_rgb, (224, 224))\n",
" else:\n",
" bg_removed = cv2.resize(image_rgb, (224, 224))\n",
" \n",
" # Edge detection\n",
" if edge_detector is not None:\n",
" try:\n",
" edge_image, edges = edge_detector.detect_edges(bg_removed)\n",
" except Exception as e:\n",
" print(f\"⚠️ Edge detection error: {e}\")\n",
" edge_image = bg_removed\n",
" else:\n",
" edge_image = bg_removed\n",
" \n",
" # Final preprocessing\n",
" if preprocessor is not None:\n",
" processed_image = preprocessor.preprocess_for_model(edge_image)\n",
" else:\n",
" processed_image = cv2.resize(edge_image, (224, 224)).astype(np.float32) / 255.0\n",
" \n",
" # Process tabular features\n",
" temperature_features = np.array([[air_temp, soil_temp]], dtype=np.float32)\n",
" days_array = np.array([[days]], dtype=np.float32)\n",
" \n",
" temperature_scaled = continuous_scaler.transform(temperature_features)\n",
" nitrogen_categorical = np.array([[nitrogen_category]], dtype=np.float32)\n",
" days_scaled = days_scaler.transform(days_array)\n",
" \n",
" tabular_processed = np.concatenate([\n",
" temperature_scaled, nitrogen_categorical, days_scaled\n",
" ], axis=1).astype('float32')\n",
" \n",
" print(f\"📊 Processed features shape: {tabular_processed.shape}\")\n",
" \n",
" # Make prediction\n",
" nitrogen_content = 3.62\n",
" prediction_successful = False\n",
"\n",
" if model is not None:\n",
" try:\n",
" image_input = np.expand_dims(processed_image, axis=0)\n",
" feature_input = np.expand_dims(tabular_processed[0], axis=0)\n",
" \n",
" prediction = model.predict([image_input, feature_input], verbose=0)\n",
" raw_prediction = prediction[0][0]\n",
" print(f\"🔍 Raw model output: {raw_prediction:.6f}\")\n",
" \n",
" if scaler_y is not None:\n",
" nitrogen_content = scaler_y.inverse_transform(prediction)[0][0]\n",
" print(f\"🔍 Denormalized: {nitrogen_content:.4f}%\")\n",
" prediction_successful = True\n",
" \n",
" if nitrogen_content < 1.0 or nitrogen_content > 6.0:\n",
" print(f\"⚠️ Warning: Predicted nitrogen {nitrogen_content:.2f}% is outside expected range\")\n",
" nitrogen_content = 3.62\n",
" prediction_successful = False\n",
" else:\n",
" nitrogen_content = raw_prediction\n",
" prediction_successful = True\n",
" \n",
" except Exception as e:\n",
" print(f\"❌ Prediction error: {e}\")\n",
" traceback.print_exc()\n",
" nitrogen_content = 3.62\n",
" else:\n",
" print(\"⚠️ Model not loaded, using default value\")\n",
" nitrogen_content = 3.62\n",
"\n",
" print(f\"✅ Final nitrogen content: {nitrogen_content:.2f}%\")\n",
" \n",
" classification = classify_nitrogen_level(nitrogen_content)\n",
" \n",
" session['prediction_result'] = {\n",
" 'nitrogen_content': round(float(nitrogen_content), 2),\n",
" 'fertilizer_applied': fertilizer_amount,\n",
" 'days': days,\n",
" 'classification': classification,\n",
" 'prediction_successful': prediction_successful\n",
" }\n",
" session.modified = True\n",
" \n",
" return redirect(url_for('result'))\n",
" \n",
" except Exception as e:\n",
" print(f\"❌ Prediction route error: {e}\")\n",
" traceback.print_exc()\n",
" return jsonify({'error': str(e)}), 500\n",
"\n",
"@app.route('/result')\n",
"def result():\n",
" prediction_result = session.get('prediction_result', None)\n",
" if prediction_result is None:\n",
" return redirect(url_for('estimation'))\n",
" return render_template('result.html', result=prediction_result)\n",
" \n",
"@app.route('/about')\n",
"def about():\n",
" return render_template('about.html')\n",
"\n",
"@app.route('/how-it-works')\n",
"def how_it_works():\n",
" return render_template('how-it-works.html')\n",
"\n",
"@app.route('/contact')\n",
"def contact():\n",
" return render_template('contact.html')\n",
"\n",
"@app.route('/team')\n",
"def team():\n",
" return render_template('team.html')\n",
"\n",
"if __name__ == '__main__':\n",
" os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)\n",
" print(f\"📁 Upload folder ready: {app.config['UPLOAD_FOLDER']}\")\n",
" print(f\"📁 U2Net weights should be at: u2net/weights/u2net.pth\")\n",
" print(f\"\\\\n🚀 Starting NitroSense AI application...\")\n",
" print(f\" Access at: http://localhost:5000\")\n",
" print(f\" Press CTRL+C to quit\\\\n\")\n",
" \n",
" try:\n",
" app.run(\n",
" debug=True, \n",
" host='0.0.0.0', \n",
" port=5000,\n",
" use_reloader=False\n",
" )\n",
" except SystemExit:\n",
" print(\"\\\\n👋 Application stopped normally\")\n",
" except Exception as e:\n",
" print(f\"\\\\n❌ Error running application: {e}\")\n",
" traceback.print_exc()\n",
"'''\n",
"\n",
"# Write the file\n",
"with open('app.py', 'w', encoding='utf-8') as f:\n",
" f.write(app_code)\n",
"\n",
"print(\"✅ app.py created successfully with session check and flash messages!\")\n",
"print(f\"File location: {os.path.join(os.getcwd(), 'app.py')}\")\n",
"\n",
"# Verify file was created\n",
"if os.path.exists('app.py'):\n",
" file_size = os.path.getsize('app.py') / 1024\n",
" print(f\"✅ Verified: app.py exists ({file_size:.2f} KB)\")\n",
"else:\n",
" print(\"❌ Failed to create app.py\")"
]
},
{
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"id": "4e37f289",
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"name": "ipython",
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"file_extension": ".py",
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"name": "python",
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|