Update app.py

app.py

@@ -1,206 +1,206 @@
-import os
-import numpy as np
-import pandas as pd
-from flask import Flask, request, jsonify
-from tensorflow.keras.models import load_model
-from tensorflow.keras.utils import load_img, img_to_array
-from math import radians, cos, sin, sqrt, atan2
-import joblib
-import requests
-from dotenv import load_dotenv
-from werkzeug.utils import secure_filename
-
-# Load environment variables
-load_dotenv()
-OPENWEATHER_API_KEY = os.getenv("OPENWEATHER_API_KEY")
-
-# Inisialisasi Flask
-app = Flask(__name__)
-UPLOAD_FOLDER = 'uploads'
-os.makedirs(UPLOAD_FOLDER, exist_ok=True)
-
-# Load model dan data
-soil_model = load_model("models/soil_type_classifier.h5")
-crop_model = joblib.load("models/model_random_forest.joblib")
-crop_model_label = joblib.load("models/label_encoder.joblib")
-soil_df = pd.read_csv("data/soil_data_with_class.csv")
-agri_df = pd.read_csv("data/tips_menanam_dan_manfaat_tanaman.csv")
-class_labels = ['Black Soil', 'Cinder Soil', 'Laterite Soil', 'Peat Soil', 'Yellow Soil']
-
-# Fungsi preprocessing gambar
-def preprocess_image(img_path):
-    img = load_img(img_path, target_size=(224, 224))
-    img_array = img_to_array(img) / 255.0
-    return np.expand_dims(img_array, axis=0)
-
-# Fungsi prediksi jenis tanah
-def predict_soil_type(img_path):
-    img_array = preprocess_image(img_path)
-    prediction = soil_model.predict(img_array)
-    predicted_index = np.argmax(prediction)
-    predicted_class = class_labels[predicted_index]
-    accuracy = float(prediction[0][predicted_index])
-    return predicted_class, accuracy
-
-# Fungsi hitung jarak terdekat
-def find_nearest_soil_data_weighted(soil_type, lat, lon, n_points=1):
-    filtered = soil_df[soil_df['Class_Name'] == soil_type].copy()
-    if filtered.empty:
-        return None
-
-    user_lat, user_lon = radians(lat), radians(lon)
-
-    def haversine(row):
-        lat2, lon2 = radians(row['Location_Latitude']), radians(row['Location_Longitude'])
-        dlat = lat2 - user_lat
-        dlon = lon2 - user_lon
-        a = sin(dlat / 2) ** 2 + cos(user_lat) * cos(lat2) * sin(dlon / 2) ** 2
-        c = 2 * atan2(sqrt(a), sqrt(1 - a))
-        return 6371 * c
-
-    filtered['Distance_km'] = filtered.apply(haversine, axis=1)
-    nearest = filtered.nsmallest(n_points, 'Distance_km').copy()
-
-    row = nearest.iloc[0]
-    return {
-        "latitude": float(row['Location_Latitude']),
-        "longitude": float(row['Location_Longitude']),
-        "pH": float(row['pH']),
-        "N": float(row['Nitrogen_N_ppm']),
-        "P": float(row['Phosphorus_P_ppm']),
-        "K": float(row['Potassium_K_ppm']),
-        "distance_km": float(row['Distance_km'])
-    }
-
-# Ambil data cuaca
-def get_weather_data(lat, lon):
-    url = f"https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={OPENWEATHER_API_KEY}&units=metric"
-    res = requests.get(url)
-    if res.status_code != 200:
-        return None
-    data = res.json()
-    return {
-        "temperature": float(data['main']['temp']),
-        "humidity": float(data['main']['humidity']),
-    }
-
-# Fungsi untuk mengambil tips bertani dan manfaat dari agri_df
-def get_farming_tips(df, crop_name):
-    crop_name_str = str(crop_name)
-    match = df[df['Nama Tanaman'].str.lower() == crop_name_str.lower()]
-    if not match.empty:
-        row = match.iloc[0]
-        return {
-            "id": int(row['ID']) if 'ID' in row else None,
-            "Nama Tanaman": row['Nama Tanaman'],
-            "Tips Menanam": row.get('Tips Menanam', 'Tidak tersedia'),
-            "Manfaat": row.get('Manfaat', 'Tidak tersedia')
-        }
-    return None
-
-def get_location_name(lat, lon):
-    """
-    Mengambil nama lokasi (reverse geocoding) dari OpenStreetMap Nominatim.
-    """
-    try:
-        url = f"https://nominatim.openstreetmap.org/reverse?lat={lat}&lon={lon}&format=json"
-        headers = {"User-Agent": "soil-api/1.0"}
-        response = requests.get(url, headers=headers, timeout=10)
-        if response.status_code == 200:
-            data = response.json()
-            return data.get("display_name", "Tidak ditemukan")
-        else:
-            return "Tidak ditemukan"
-    except Exception:
-        return "Tidak ditemukan"
-
-# Endpoint utama
-@app.route('/analyze', methods=['POST'])
-def analyze():
-    if 'image' not in request.files:
-        return jsonify({"error": "Gambar tidak ditemukan"}), 400
-
-    image_file = request.files['image']
-    if image_file.filename == '':
-        return jsonify({"error": "Nama file gambar kosong"}), 400
-
-    try:
-        lat = float(request.form.get('lat'))
-        lon = float(request.form.get('lon'))
-        if not (-90 <= lat <= 90 and -180 <= lon <= 180):
-            raise ValueError("Out of bounds")
-    except:
-        return jsonify({"error": "Koordinat tidak valid"}), 400
-
-    filename = secure_filename(image_file.filename)
-    image_path = os.path.join(UPLOAD_FOLDER, filename)
-    image_file.save(image_path)
-
-    predicted_soil, soil_accuracy = predict_soil_type(image_path)
-    nearest_data = find_nearest_soil_data_weighted(predicted_soil, lat, lon)
-    if nearest_data is None:
-        return jsonify({"error": "Data jenis tanah tidak ditemukan"}), 404
-
-    # Ambil nama lokasi dari OSM
-    location_name = get_location_name(nearest_data['latitude'], nearest_data['longitude'])
-
-    weather = get_weather_data(lat, lon)
-    if not weather:
-        return jsonify({"error": "Gagal mengambil data cuaca"}), 500
-
-    input_data = pd.DataFrame([{
-        'temperature': weather['temperature'],
-        'humidity': weather['humidity'],
-        'ph': nearest_data['pH'],
-        'N': nearest_data['N'],
-        'P': nearest_data['P'],
-        'K': nearest_data['K'],
-    }])
-
-    if hasattr(crop_model, "predict_proba"):
-        proba = crop_model.predict_proba(input_data)[0]
-        top_idx = np.argsort(proba)[::-1][:5]
-        recommended_crops = [
-            str(crop_model_label.inverse_transform([crop_model.classes_[i]])[0])
-            if hasattr(crop_model_label, "inverse_transform") else str(crop_model.classes_[i])
-            for i in top_idx
-        ]
-        crop_percentages = [round(float(proba[i]) * 100, 2) for i in top_idx]
-    else:
-        pred = crop_model.predict(input_data)[0]
-        recommended_crops = [str(crop_model_label.inverse_transform([pred])[0])] if hasattr(crop_model_label, "inverse_transform") else [str(pred)]
-        crop_percentages = [100.0]
-
-    tips_list = []
-    for crop in recommended_crops:
-        tips = get_farming_tips(agri_df, crop)
-        tips_list.append(tips if tips else {"Tanaman": crop, "Pesan": "Data tidak tersedia"})
-
-    result = {
-        "Class_Name": predicted_soil,
-        "soil_prediction_accuracy": round(soil_accuracy * 100, 2),
-        "nearest_soil_data": {
-            "latitude": nearest_data['latitude'],
-            "longitude": nearest_data['longitude'],
-            "location_name": location_name,  # Tambahkan nama lokasi di sini
-            "pH": nearest_data['pH'],
-            "N": nearest_data['N'],
-            "P": nearest_data['P'],
-            "K": nearest_data['K'],
-            "distance_km": round(nearest_data['distance_km'], 2)
-        },
-        "weather": weather,
-        "recommended_crops": [
-            {
-                "crop": crop,
-                "recommendation_percentage": percent
-            }
-            for crop, percent in zip(recommended_crops, crop_percentages)
-        ],
-        "farming_tips": tips_list
-    }
-
-    return jsonify(result)
-
-if __name__ == '__main__':
-    app.run(debug=True)
+import os
+import numpy as np
+import pandas as pd
+from flask import Flask, request, jsonify
+from tensorflow.keras.models import load_model
+from tensorflow.keras.utils import load_img, img_to_array
+from math import radians, cos, sin, sqrt, atan2
+import joblib
+import requests
+from dotenv import load_dotenv
+from werkzeug.utils import secure_filename
+
+# Load environment variables
+load_dotenv()
+OPENWEATHER_API_KEY = os.getenv("OPENWEATHER_API_KEY")
+
+# Inisialisasi Flask
+app = Flask(__name__)
+UPLOAD_FOLDER = '/tmp/uploads'
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+# Load model dan data
+soil_model = load_model("models/soil_type_classifier.h5")
+crop_model = joblib.load("models/model_random_forest.joblib")
+crop_model_label = joblib.load("models/label_encoder.joblib")
+soil_df = pd.read_csv("data/soil_data_with_class.csv")
+agri_df = pd.read_csv("data/tips_menanam_dan_manfaat_tanaman.csv")
+class_labels = ['Black Soil', 'Cinder Soil', 'Laterite Soil', 'Peat Soil', 'Yellow Soil']
+
+# Fungsi preprocessing gambar
+def preprocess_image(img_path):
+    img = load_img(img_path, target_size=(224, 224))
+    img_array = img_to_array(img) / 255.0
+    return np.expand_dims(img_array, axis=0)
+
+# Fungsi prediksi jenis tanah
+def predict_soil_type(img_path):
+    img_array = preprocess_image(img_path)
+    prediction = soil_model.predict(img_array)
+    predicted_index = np.argmax(prediction)
+    predicted_class = class_labels[predicted_index]
+    accuracy = float(prediction[0][predicted_index])
+    return predicted_class, accuracy
+
+# Fungsi hitung jarak terdekat
+def find_nearest_soil_data_weighted(soil_type, lat, lon, n_points=1):
+    filtered = soil_df[soil_df['Class_Name'] == soil_type].copy()
+    if filtered.empty:
+        return None
+
+    user_lat, user_lon = radians(lat), radians(lon)
+
+    def haversine(row):
+        lat2, lon2 = radians(row['Location_Latitude']), radians(row['Location_Longitude'])
+        dlat = lat2 - user_lat
+        dlon = lon2 - user_lon
+        a = sin(dlat / 2) ** 2 + cos(user_lat) * cos(lat2) * sin(dlon / 2) ** 2
+        c = 2 * atan2(sqrt(a), sqrt(1 - a))
+        return 6371 * c
+
+    filtered['Distance_km'] = filtered.apply(haversine, axis=1)
+    nearest = filtered.nsmallest(n_points, 'Distance_km').copy()
+
+    row = nearest.iloc[0]
+    return {
+        "latitude": float(row['Location_Latitude']),
+        "longitude": float(row['Location_Longitude']),
+        "pH": float(row['pH']),
+        "N": float(row['Nitrogen_N_ppm']),
+        "P": float(row['Phosphorus_P_ppm']),
+        "K": float(row['Potassium_K_ppm']),
+        "distance_km": float(row['Distance_km'])
+    }
+
+# Ambil data cuaca
+def get_weather_data(lat, lon):
+    url = f"https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={OPENWEATHER_API_KEY}&units=metric"
+    res = requests.get(url)
+    if res.status_code != 200:
+        return None
+    data = res.json()
+    return {
+        "temperature": float(data['main']['temp']),
+        "humidity": float(data['main']['humidity']),
+    }
+
+# Fungsi untuk mengambil tips bertani dan manfaat dari agri_df
+def get_farming_tips(df, crop_name):
+    crop_name_str = str(crop_name)
+    match = df[df['Nama Tanaman'].str.lower() == crop_name_str.lower()]
+    if not match.empty:
+        row = match.iloc[0]
+        return {
+            "id": int(row['ID']) if 'ID' in row else None,
+            "Nama Tanaman": row['Nama Tanaman'],
+            "Tips Menanam": row.get('Tips Menanam', 'Tidak tersedia'),
+            "Manfaat": row.get('Manfaat', 'Tidak tersedia')
+        }
+    return None
+
+def get_location_name(lat, lon):
+    """
+    Mengambil nama lokasi (reverse geocoding) dari OpenStreetMap Nominatim.
+    """
+    try:
+        url = f"https://nominatim.openstreetmap.org/reverse?lat={lat}&lon={lon}&format=json"
+        headers = {"User-Agent": "soil-api/1.0"}
+        response = requests.get(url, headers=headers, timeout=10)
+        if response.status_code == 200:
+            data = response.json()
+            return data.get("display_name", "Tidak ditemukan")
+        else:
+            return "Tidak ditemukan"
+    except Exception:
+        return "Tidak ditemukan"
+
+# Endpoint utama
+@app.route('/analyze', methods=['POST'])
+def analyze():
+    if 'image' not in request.files:
+        return jsonify({"error": "Gambar tidak ditemukan"}), 400
+
+    image_file = request.files['image']
+    if image_file.filename == '':
+        return jsonify({"error": "Nama file gambar kosong"}), 400
+
+    try:
+        lat = float(request.form.get('lat'))
+        lon = float(request.form.get('lon'))
+        if not (-90 <= lat <= 90 and -180 <= lon <= 180):
+            raise ValueError("Out of bounds")
+    except:
+        return jsonify({"error": "Koordinat tidak valid"}), 400
+
+    filename = secure_filename(image_file.filename)
+    image_path = os.path.join(UPLOAD_FOLDER, filename)
+    image_file.save(image_path)
+
+    predicted_soil, soil_accuracy = predict_soil_type(image_path)
+    nearest_data = find_nearest_soil_data_weighted(predicted_soil, lat, lon)
+    if nearest_data is None:
+        return jsonify({"error": "Data jenis tanah tidak ditemukan"}), 404
+
+    # Ambil nama lokasi dari OSM
+    location_name = get_location_name(nearest_data['latitude'], nearest_data['longitude'])
+
+    weather = get_weather_data(lat, lon)
+    if not weather:
+        return jsonify({"error": "Gagal mengambil data cuaca"}), 500
+
+    input_data = pd.DataFrame([{
+        'temperature': weather['temperature'],
+        'humidity': weather['humidity'],
+        'ph': nearest_data['pH'],
+        'N': nearest_data['N'],
+        'P': nearest_data['P'],
+        'K': nearest_data['K'],
+    }])
+
+    if hasattr(crop_model, "predict_proba"):
+        proba = crop_model.predict_proba(input_data)[0]
+        top_idx = np.argsort(proba)[::-1][:5]
+        recommended_crops = [
+            str(crop_model_label.inverse_transform([crop_model.classes_[i]])[0])
+            if hasattr(crop_model_label, "inverse_transform") else str(crop_model.classes_[i])
+            for i in top_idx
+        ]
+        crop_percentages = [round(float(proba[i]) * 100, 2) for i in top_idx]
+    else:
+        pred = crop_model.predict(input_data)[0]
+        recommended_crops = [str(crop_model_label.inverse_transform([pred])[0])] if hasattr(crop_model_label, "inverse_transform") else [str(pred)]
+        crop_percentages = [100.0]
+
+    tips_list = []
+    for crop in recommended_crops:
+        tips = get_farming_tips(agri_df, crop)
+        tips_list.append(tips if tips else {"Tanaman": crop, "Pesan": "Data tidak tersedia"})
+
+    result = {
+        "Class_Name": predicted_soil,
+        "soil_prediction_accuracy": round(soil_accuracy * 100, 2),
+        "nearest_soil_data": {
+            "latitude": nearest_data['latitude'],
+            "longitude": nearest_data['longitude'],
+            "location_name": location_name,  # Tambahkan nama lokasi di sini
+            "pH": nearest_data['pH'],
+            "N": nearest_data['N'],
+            "P": nearest_data['P'],
+            "K": nearest_data['K'],
+            "distance_km": round(nearest_data['distance_km'], 2)
+        },
+        "weather": weather,
+        "recommended_crops": [
+            {
+                "crop": crop,
+                "recommendation_percentage": percent
+            }
+            for crop, percent in zip(recommended_crops, crop_percentages)
+        ],
+        "farming_tips": tips_list
+    }
+
+    return jsonify(result)
+
+if __name__ == '__main__':
+    app.run(debug=True)