Update app.py

app.py

@@ -1,201 +1,202 @@
-from flask import Flask, render_template, request, redirect, url_for, jsonify
-from tensorflow.keras.models import load_model
-import numpy as np
-import joblib
-import pandas as pd
-import io
-import requests
-import threading
-import time
-from PIL import Image  # Import for image processing
-
-app = Flask(__name__)
-
-# Load models
-pump_model = joblib.load('pump_status_dt_model.pkl')
-soil_model = load_model('soil_classification_model.h5')
-
-# Dictionaries for crop types, regions, etc.
-crop_types = {'BANANA': 0, 'BEAN': 1, 'CABBAGE': 2, 'CITRUS': 3, 'COTTON': 4,
-              'MAIZE': 5, 'MELON': 6, 'MUSTARD': 7, 'ONION': 8, 'OTHER': 9,
-              'POTATO': 10, 'RICE': 11, 'SOYABEAN': 12, 'SUGARCANE': 13,
-              'TOMATO': 14, 'WHEAT': 15}
-
-soil_types = {'DRY': 0, 'HUMID': 1, 'WET': 2}
-regions = {'DESERT': 0, 'HUMID': 1, 'SEMI ARID': 2, 'SEMI HUMID': 3}
-weather_conditions = {'SUNNY': 0, 'RAINY': 1, 'WINDY': 2, 'NORMAL': 3}
-irrigation_types = {'Drip Irrigation': 0, 'Manual Irrigation': 1,
-                    'Sprinkler Irrigation': 2, 'Subsurface Irrigation': 3,
-                    'Surface Irrigation': 4}
-
-soil_labels = {1: 'Black Soil', 2: 'Clay Soil', 0: 'Alluvial Soil', 3: 'Red Soil'}
-
-# Global variables
-soil_moisture_data = []
-pump_status = "Off"
-previous_pump_status = "Off"
-graph_data = []
-
-
-# Function to fetch weather data
-def get_weather(city):
-    api_key = "b3c62ae7f7ad5fc3cb0a7b56cb7cbda6"
-    url = f"https://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}&units=metric"
-    try:
-        response = requests.get(url)
-        response.raise_for_status()
-        data = response.json()
-        temp = data['main']['temp']
-        pressure = data['main']['pressure']
-        humidity = data['main']['humidity']
-        weather_desc = data['weather'][0]['main']
-        return temp, pressure, humidity, weather_desc
-    except requests.exceptions.HTTPError:
-        return None, None, None, None
-
-
-# Function to map soil type to pump model's expected format
-def map_soil_to_pump_model(soil_label):
-    if soil_label in ['Black Soil', 'Red Soil']:
-        return 'DRY'
-    elif soil_label == 'Clay Soil':
-        return 'WET'
-    elif soil_label == 'Alluvial Soil':
-        return 'HUMID'
-    return None
-
-
-# Function to run predictions for all soil moisture values
-# Function to run predictions for all soil moisture values
-def run_predictions(crop_type, soil_type_for_pump, region, temperature, pressure, humidity, crop_age, irrigation_type, auto_weather_condition):
-    global pump_status, graph_data, previous_pump_status
-    pump_status = "Off"
-    previous_pump_status = "Off"
-    graph_data = []
-
-    for soil_moisture in soil_moisture_data:
-        try:
-            soil_moisture_value = float(soil_moisture)  # Ensure this is a float
-        except ValueError:
-            print(f"Skipping invalid soil moisture value: {soil_moisture}")
-            continue
-
-        # Prepare features for pump prediction
-        features = np.array([crop_types[crop_type], soil_types[soil_type_for_pump],
-                             regions[region], temperature if temperature else 0,
-                             weather_conditions.get(auto_weather_condition, 0),
-                             pressure if pressure else 0, humidity if humidity else 0,
-                             int(crop_age), irrigation_types[irrigation_type],
-                             soil_moisture_value]).reshape(1, -1)
-
-        # Make the pump prediction
-        pump_prediction = pump_model.predict(features)
-        pump_status = 'On' if pump_prediction[0] == 1 else 'Off'
-        graph_data.append((soil_moisture_value, 1 if pump_status == 'On' else -1))  # Update status to -1 for Off
-
-        print(f"Predicted Pump Status: {pump_status} for Soil Moisture: {soil_moisture_value}")  # Debugging output
-
-        # Play sound if pump is Off and it wasn't Off previously
-        if pump_status == "Off" and previous_pump_status != "Off":
-            play_sound()
-
-        previous_pump_status = pump_status
-
-        # Wait for 1 second before next prediction
-        time.sleep(2)
-
-
-def play_sound():
-    # You can use any sound file here
-    print("Beep! Pump is Off.")  # Placeholder for actual sound functionality
-
-
-# Main route
-@app.route('/', methods=['GET', 'POST'])
-def index():
-    global soil_moisture_data
-
-    city = crop_type = region = crop_age = irrigation_type = None
-    temperature = pressure = humidity = weather_desc = auto_weather_condition = None
-    soil_image_url = None
-
-    if request.method == 'POST':
-        city = request.form.get('city', '')
-        crop_type = request.form.get('crop_type', '')
-        region = request.form.get('region', '')
-        crop_age = request.form.get('crop_age', '')
-        irrigation_type = request.form.get('irrigation_type', '')
-
-        # Handle CSV file upload
-        if 'soil_moisture' in request.files:
-            soil_moisture_file = request.files['soil_moisture']
-            if soil_moisture_file:
-                # Read CSV file
-                df = pd.read_csv(soil_moisture_file)
-                soil_moisture_data = df['Soil Moisture'].tolist()
-
-        # Handle soil image upload
-        soil_image_file = request.files.get('soil_image')
-        if soil_image_file:
-            # Load and preprocess the image for prediction
-            image = Image.open(io.BytesIO(soil_image_file.read()))
-            image = image.resize((150, 150))
-            image = np.array(image) / 255.0
-            if image.shape[-1] == 4:
-                image = image[..., :3]
-            image = np.expand_dims(image, axis=0)
-
-            # Predict the soil type
-            soil_pred = soil_model.predict(image)
-            soil_label = soil_labels[np.argmax(soil_pred)]
-            soil_type_for_pump = map_soil_to_pump_model(soil_label)
-        else:
-            soil_type_for_pump = request.form.get('soil_type')
-
-        if city:
-            temperature, pressure, humidity, weather_desc = get_weather(city)
-            auto_weather_condition = "NORMAL"  # Default weather condition
-            if weather_desc:
-                if 'sunny' in weather_desc.lower():
-                    auto_weather_condition = 'SUNNY'
-                elif 'rain' in weather_desc.lower():
-                    auto_weather_condition = 'RAINY'
-                elif 'wind' in weather_desc.lower():
-                    auto_weather_condition = 'WINDY'
-
-        if 'predict' in request.form:
-            # Start a thread for predictions
-            threading.Thread(target=run_predictions, args=(
-                crop_type, soil_type_for_pump, region, temperature, pressure, humidity, crop_age, irrigation_type, auto_weather_condition)).start()
-            return redirect(url_for('predict'))
-
-    return render_template('index.html', temperature=temperature, pressure=pressure,
-                           humidity=humidity, weather_desc=weather_desc, crop_types=crop_types,
-                           regions=regions, irrigation_types=irrigation_types, soil_types=soil_types,
-                           crop_type=crop_type, region=region, crop_age=crop_age,
-                           irrigation_type=irrigation_type, city=city, soil_image_url=soil_image_url)
-
-
-# Prediction route
-@app.route('/predict', methods=['GET'])
-def predict():
-    global pump_status, graph_data
-    return render_template('predict.html', pump_status=pump_status, graph_data=graph_data)
-
-
-# Update graph data every second
-@app.route('/update_graph', methods=['GET'])
-def update_graph():
-    global graph_data
-    return jsonify(graph_data)
-
-
-# Update pump status every second
-@app.route('/update_pump_status', methods=['GET'])
-def update_pump_status():
-    global pump_status
-    return jsonify({'pump_status': pump_status})
-
-
-if __name__ == '__main__':
-    app.run(debug=True)
+from flask import Flask, render_template, request, redirect, url_for, jsonify
+from tensorflow.keras.models import load_model
+import numpy as np
+import joblib
+import pandas as pd
+import io
+import requests
+import threading
+import time
+from PIL import Image  # Import for image processing
+
+app = Flask(__name__)
+
+# Load models
+pump_model = joblib.load('pump_status_dt_model.pkl')
+soil_model = load_model('soil_classification_model.h5')
+
+# Dictionaries for crop types, regions, etc.
+crop_types = {'BANANA': 0, 'BEAN': 1, 'CABBAGE': 2, 'CITRUS': 3, 'COTTON': 4,
+              'MAIZE': 5, 'MELON': 6, 'MUSTARD': 7, 'ONION': 8, 'OTHER': 9,
+              'POTATO': 10, 'RICE': 11, 'SOYABEAN': 12, 'SUGARCANE': 13,
+              'TOMATO': 14, 'WHEAT': 15}
+
+soil_types = {'DRY': 0, 'HUMID': 1, 'WET': 2}
+regions = {'DESERT': 0, 'HUMID': 1, 'SEMI ARID': 2, 'SEMI HUMID': 3}
+weather_conditions = {'SUNNY': 0, 'RAINY': 1, 'WINDY': 2, 'NORMAL': 3}
+irrigation_types = {'Drip Irrigation': 0, 'Manual Irrigation': 1,
+                    'Sprinkler Irrigation': 2, 'Subsurface Irrigation': 3,
+                    'Surface Irrigation': 4}
+
+soil_labels = {1: 'Black Soil', 2: 'Clay Soil', 0: 'Alluvial Soil', 3: 'Red Soil'}
+
+# Global variables
+soil_moisture_data = []
+pump_status = "Off"
+previous_pump_status = "Off"
+graph_data = []
+
+
+# Function to fetch weather data
+def get_weather(city):
+    api_key=os.getenv('WEATHER_API')
+    api_key = api_key
+    url = f"https://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}&units=metric"
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+        temp = data['main']['temp']
+        pressure = data['main']['pressure']
+        humidity = data['main']['humidity']
+        weather_desc = data['weather'][0]['main']
+        return temp, pressure, humidity, weather_desc
+    except requests.exceptions.HTTPError:
+        return None, None, None, None
+
+
+# Function to map soil type to pump model's expected format
+def map_soil_to_pump_model(soil_label):
+    if soil_label in ['Black Soil', 'Red Soil']:
+        return 'DRY'
+    elif soil_label == 'Clay Soil':
+        return 'WET'
+    elif soil_label == 'Alluvial Soil':
+        return 'HUMID'
+    return None
+
+
+# Function to run predictions for all soil moisture values
+# Function to run predictions for all soil moisture values
+def run_predictions(crop_type, soil_type_for_pump, region, temperature, pressure, humidity, crop_age, irrigation_type, auto_weather_condition):
+    global pump_status, graph_data, previous_pump_status
+    pump_status = "Off"
+    previous_pump_status = "Off"
+    graph_data = []
+
+    for soil_moisture in soil_moisture_data:
+        try:
+            soil_moisture_value = float(soil_moisture)  # Ensure this is a float
+        except ValueError:
+            print(f"Skipping invalid soil moisture value: {soil_moisture}")
+            continue
+
+        # Prepare features for pump prediction
+        features = np.array([crop_types[crop_type], soil_types[soil_type_for_pump],
+                             regions[region], temperature if temperature else 0,
+                             weather_conditions.get(auto_weather_condition, 0),
+                             pressure if pressure else 0, humidity if humidity else 0,
+                             int(crop_age), irrigation_types[irrigation_type],
+                             soil_moisture_value]).reshape(1, -1)
+
+        # Make the pump prediction
+        pump_prediction = pump_model.predict(features)
+        pump_status = 'On' if pump_prediction[0] == 1 else 'Off'
+        graph_data.append((soil_moisture_value, 1 if pump_status == 'On' else -1))  # Update status to -1 for Off
+
+        print(f"Predicted Pump Status: {pump_status} for Soil Moisture: {soil_moisture_value}")  # Debugging output
+
+        # Play sound if pump is Off and it wasn't Off previously
+        if pump_status == "Off" and previous_pump_status != "Off":
+            play_sound()
+
+        previous_pump_status = pump_status
+
+        # Wait for 1 second before next prediction
+        time.sleep(2)
+
+
+def play_sound():
+    # You can use any sound file here
+    print("Beep! Pump is Off.")  # Placeholder for actual sound functionality
+
+
+# Main route
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    global soil_moisture_data
+
+    city = crop_type = region = crop_age = irrigation_type = None
+    temperature = pressure = humidity = weather_desc = auto_weather_condition = None
+    soil_image_url = None
+
+    if request.method == 'POST':
+        city = request.form.get('city', '')
+        crop_type = request.form.get('crop_type', '')
+        region = request.form.get('region', '')
+        crop_age = request.form.get('crop_age', '')
+        irrigation_type = request.form.get('irrigation_type', '')
+
+        # Handle CSV file upload
+        if 'soil_moisture' in request.files:
+            soil_moisture_file = request.files['soil_moisture']
+            if soil_moisture_file:
+                # Read CSV file
+                df = pd.read_csv(soil_moisture_file)
+                soil_moisture_data = df['Soil Moisture'].tolist()
+
+        # Handle soil image upload
+        soil_image_file = request.files.get('soil_image')
+        if soil_image_file:
+            # Load and preprocess the image for prediction
+            image = Image.open(io.BytesIO(soil_image_file.read()))
+            image = image.resize((150, 150))
+            image = np.array(image) / 255.0
+            if image.shape[-1] == 4:
+                image = image[..., :3]
+            image = np.expand_dims(image, axis=0)
+
+            # Predict the soil type
+            soil_pred = soil_model.predict(image)
+            soil_label = soil_labels[np.argmax(soil_pred)]
+            soil_type_for_pump = map_soil_to_pump_model(soil_label)
+        else:
+            soil_type_for_pump = request.form.get('soil_type')
+
+        if city:
+            temperature, pressure, humidity, weather_desc = get_weather(city)
+            auto_weather_condition = "NORMAL"  # Default weather condition
+            if weather_desc:
+                if 'sunny' in weather_desc.lower():
+                    auto_weather_condition = 'SUNNY'
+                elif 'rain' in weather_desc.lower():
+                    auto_weather_condition = 'RAINY'
+                elif 'wind' in weather_desc.lower():
+                    auto_weather_condition = 'WINDY'
+
+        if 'predict' in request.form:
+            # Start a thread for predictions
+            threading.Thread(target=run_predictions, args=(
+                crop_type, soil_type_for_pump, region, temperature, pressure, humidity, crop_age, irrigation_type, auto_weather_condition)).start()
+            return redirect(url_for('predict'))
+
+    return render_template('index.html', temperature=temperature, pressure=pressure,
+                           humidity=humidity, weather_desc=weather_desc, crop_types=crop_types,
+                           regions=regions, irrigation_types=irrigation_types, soil_types=soil_types,
+                           crop_type=crop_type, region=region, crop_age=crop_age,
+                           irrigation_type=irrigation_type, city=city, soil_image_url=soil_image_url)
+
+
+# Prediction route
+@app.route('/predict', methods=['GET'])
+def predict():
+    global pump_status, graph_data
+    return render_template('predict.html', pump_status=pump_status, graph_data=graph_data)
+
+
+# Update graph data every second
+@app.route('/update_graph', methods=['GET'])
+def update_graph():
+    global graph_data
+    return jsonify(graph_data)
+
+
+# Update pump status every second
+@app.route('/update_pump_status', methods=['GET'])
+def update_pump_status():
+    global pump_status
+    return jsonify({'pump_status': pump_status})
+
+
+if __name__ == '__main__':
+    app.run(debug=True)