From f16dfc4bb453107e3f2621b78c2425eb5c03b019 Mon Sep 17 00:00:00 2001
From: satrio142 <106372451+satrio142@users.noreply.github.com>
Date: Wed, 29 May 2024 20:49:58 +0700
Subject: [PATCH] Update app.py

---
 app.py | 58 ++++++++++++++++++++++++++++++++++++++++++++++------------
 1 file changed, 46 insertions(+), 12 deletions(-)

diff --git a/app.py b/app.py
index aa92e4c..a140faa 100644
--- a/app.py
+++ b/app.py
@@ -29,28 +29,62 @@ async def predict_image(img: UploadFile, response: Response):
         file_like_object = BytesIO(file_content)
 
         # Load the image from the file-like object
-        img = load_img(file_like_object, target_size=(150, 150))
+        # img = load_img(file_like_object, target_size=(150, 150))
 
         # Load the model
         model = load_model('combined_model.h5')
 
         # Prepare the image for prediction
-        image = img_to_array(img)
-        image = np.expand_dims(image, axis=0)
+        # image = img_to_array(img)
+        # image = np.expand_dims(image, axis=0)
 
         # Predict the class of the image
-        prediction = model.predict([image, np.zeros((1, 150, 150, 3))])[0]
-        confidence = np.max(prediction)
-        print(f"Predictions: {prediction}, Confidence: {confidence}")  # Debugging output
-        predicted_class_index = np.argmax(prediction)
-        if predicted_class_index >= len(class_labels) or confidence < confidence_threshold:
-            return "Cannot be predicted", confidence
-        predicted_label = class_labels[predicted_class_index]
-        return predicted_label, confidence
+def predict_jenis_buah(model, class_labels, file_name, target_size=(224, 224), confidence_threshold=0.6):
+    img = load_img(file_name, target_size=target_size)
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0) / 255.0
+    prediction = model.predict([img_array, np.zeros((1, 150, 150, 3))])[0]
+    confidence = np.max(prediction)
+    print(f"Predictions: {prediction}, Confidence: {confidence}")  # Debugging output
+    predicted_class_index = np.argmax(prediction)
+    if predicted_class_index >= len(class_labels) or confidence < confidence_threshold:
+        return "Cannot be predicted", confidence
+    predicted_label = class_labels[predicted_class_index]
+    return predicted_label, confidence
+
+# Fungsi untuk prediksi kesegaran buah
+def predict_kesegaran(model, file_name, target_size=(150, 150)):
+    img = load_img(file_name, target_size=target_size)
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0) / 255.0
+    prediction = model.predict([np.zeros((1, 224, 224, 3)), img_array])[1]
+    print(f"Kesegaran prediction: {prediction}")  # Debugging output
+    predicted_label = 'Segar' if prediction[0] > 0.01 else 'Tidak Segar'
+    return predicted_label
+
+# Mengunggah dan memprediksi gambar baru
+    uploaded = files.upload()
+    for fn in uploaded.keys():
+        file_path = fn
+        jenis_buah, confidence = predict_jenis_buah(combined_model, class_labels, file_path)
+        if jenis_buah == "Cannot be predicted":
+            kesegaran_buah = "Cannot be predicted"
+        else:
+            kesegaran_buah = predict_kesegaran(combined_model, file_path)
+    
+        # Menampilkan hasil prediksi
+        img = load_img(file_path)
+        plt.imshow(img)
+        plt.title(f'Predicted: {jenis_buah} (Confidence: {confidence:.2f}), Kesegaran: {kesegaran_buah}')
+        plt.axis('off')
+        plt.show()
+    
+        print(f'Gambar {fn} adalah {jenis_buah}, dan buah tersebut {kesegaran_buah}')
+
 
 # Menentukan label
 
-        return {"result":"print"}
+        return {"result":"kesegaran_buah"}
     except Exception as e:
         traceback.print_exc()
         response.status_code = 500