first commit

app.py

@@ -0,0 +1,175 @@
+from flask import Flask, render_template, request, redirect, url_for, jsonify
+import os
+import base64
+import tensorflow as tf
+from tensorflow import keras
+from keras import preprocessing
+import numpy as np
+import cv2
+from werkzeug.utils import secure_filename
+import time
+
+app = Flask(__name__)
+app.config["UPLOAD_FOLDER"] = "static/uploads/"
+
+# folder static/uploads
+if not os.path.exists(app.config["UPLOAD_FOLDER"]):
+    os.makedirs(app.config["UPLOAD_FOLDER"])
+
+# Load model Hard Cascade Classifier
+tomato_cascade = cv2.CascadeClassifier("model/tomato_classifier.xml")
+
+# Load kedua model klasifikasi
+#model_ripe = keras.models.load_model("model/CNN MobileNetV2-epochs10new-100.0.h5")
+#model_fresh = keras.models.load_model("model/CNN MobileNetV2-epochs10kesegaran-99.66.h5")
+
+modl_newgen = keras.models.load_model("model/CNN MobileNetV2-epochs5newgen-99.41.h5")
+
+# Fungsi deteksi tomat
+def is_tomato(img_path):
+    """Deteksi apakah gambar mengandung tomat atau tidak"""
+    img = cv2.imread(img_path)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    tomatoes = tomato_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+    return len(tomatoes) > 0  
+
+# Fungsi prediksi gambar
+def predict_image(img_path, model, class_names):
+    img = preprocessing.image.load_img(img_path, target_size=(224, 224))
+    img_array = preprocessing.image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.0
+    predictions = model.predict(img_array)
+    result = np.argmax(predictions, axis=1)
+    confidence = int(np.max(predictions) * 100)
+    return class_names[result[0]], confidence
+
+@app.route("/")
+def index():
+    return render_template("index.html")
+
+@app.route("/upload", methods=["GET", "POST"])
+def upload():
+    if request.method == "POST":
+        # Proses file unggahan
+        if 'file' not in request.files:
+            return render_template("upload.html", error="Tidak ada file yang dipilih.")  
+        
+        file = request.files['file']
+        if file.filename == '':
+            return render_template("upload.html", error="Nama file kosong. Silakan pilih file lain.") 
+
+        if file:
+            filename = secure_filename(file.filename)
+            filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+            file.save(filepath)
+
+            # Validasi gambar menggunakan Hard Cascade
+            if not is_tomato(filepath):
+                return render_template("upload.html", error="Gambar yang diunggah bukan tomat. Harap unggah gambar tomat.")
+
+            # Simpan path gambar di sesi sementara untuk ditampilkan di halaman hasil
+            return redirect(url_for("predict", img_path=filename))
+    
+    return render_template("upload.html")
+
+@app.route("/predict", methods=["GET", "POST"])
+def predict():
+    if request.method == "POST":
+        # Tangani data gambar dari kamera
+        data = request.get_json()
+        if data and "image" in data:
+            # Decode Base64 menjadi file gambar
+            image_data = data["image"].split(",")[1]  
+            image_bytes = base64.b64decode(image_data)
+            filename = f"camera_{int(time.time())}.png"
+            filepath = os.path.join(app.config["UPLOAD_FOLDER"], filename)
+
+            # Simpan gambar ke folder uploads
+            with open(filepath, "wb") as f:
+                f.write(image_bytes)
+
+            # Validasi gambar menggunakan Hard Cascade
+            if not is_tomato(filepath):
+                return jsonify({"success": False, "error": "Gambar yang diambil bukan tomat. Harap ambil gambar tomat."}), 400
+
+            # Lakukan prediksi setelah validasi
+            #class_names_ripe = ['Belum Matang', 'Matang']
+            #class_names_fresh = ['Busuk', 'Segar']
+            
+            class_names_tomat = ['Matang Busuk', 'Matang Segar', 'Mentah Busuk', 'Mentah Segar']
+
+            # Prediksi kematangan
+            #predicted_class_ripe, confidence_ripe = predict_image(filepath, model_ripe, class_names_ripe)
+
+            # Prediksi kesegaran
+            #predicted_class_fresh, confidence_fresh = predict_image(filepath, model_fresh, class_names_fresh)
+
+            predicted_class_tomat, confidence_tomat = predict_image(filepath, modl_newgen, class_names_tomat)
+            
+            # Gabungkan hasil prediksi
+            #predicted_class = f"{predicted_class_tomat}"
+            #average_confidence = round((confidence_ripe + confidence_fresh) / 2, 2)
+
+            # Kirim hasil prediksi sebagai respons JSON
+            return jsonify({
+                "success": True,
+                "filename": filename,
+                "predicted_class": predicted_class_tomat,
+                "confidence": confidence_tomat
+            })
+
+        return jsonify({"success": False, "error": "No valid image data received"}), 400
+
+    # Tangani metode GET untuk menampilkan halaman prediksi
+    img_path = request.args.get("img_path")
+    if not img_path:
+        return redirect(url_for("upload"))
+
+    filepath = os.path.join(app.config["UPLOAD_FOLDER"], img_path)
+    if not os.path.exists(filepath):
+        return redirect(url_for("upload"))
+
+    #class_names_ripe = ['Belum Matang', 'Matang']
+    #class_names_fresh = ['Busuk', 'Segar']
+    
+    class_names_tomat = ['Matang Busuk', 'Matang Segar', 'Mentah Busuk', 'Mentah Segar']
+
+    # Prediksi kematangan
+    #predicted_class_ripe, confidence_ripe = predict_image(filepath, model_ripe, class_names_ripe)
+
+    # Prediksi kesegaran
+    #predicted_class_fresh, confidence_fresh = predict_image(filepath, model_fresh, class_names_fresh)
+
+    #predicted_class = f"{predicted_class_ripe} & {predicted_class_fresh}"
+    #average_confidence = round((confidence_ripe + confidence_fresh) / 2, 2)
+
+    predicted_class_tomat, confidence_tomat = predict_image(filepath, modl_newgen, class_names_tomat)
+    
+    local_time = time.localtime(time.time())
+    data_date = time.strftime("%d-%m-%Y", local_time)
+    data_time = time.strftime("%H:%M:%S", local_time)
+
+    return render_template(
+        "predict.html",
+        img_path=img_path,
+        predicted_class=predicted_class_tomat,
+        confidence=confidence_tomat,
+        data_date=data_date,
+        data_time=data_time
+    )
+
+@app.route("/more")
+def more():
+    return render_template("more.html")
+
+@app.route("/faq")
+def faq():
+    return render_template("faq.html")
+
+@app.route("/about")
+def about():
+    return render_template("about.html")
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000, debug=True)

cert.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
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+-----END CERTIFICATE-----

key.pem

@@ -0,0 +1,52 @@
+-----BEGIN PRIVATE KEY-----
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+-----END PRIVATE KEY-----

p.py

@@ -0,0 +1,6 @@
+<hr class="my-4">
+    {% if error %}
+    <div class="alert alert-danger" role="alert">
+        {{ error }}
+    </div>
+    {% endif %}