bisa

app.py

@@ -32,59 +32,66 @@ async def predict_image(img: UploadFile, response: Response):
         # img = load_img(file_like_object, target_size=(150, 150))
 
         # Load the model
-        model = load_model('combined_model.h5')
+        model = load_model('combined_model_new.h5')
 
         # Prepare the image for prediction
         # image = img_to_array(img)
         # image = np.expand_dims(image, axis=0)
 
         # Predict the class of the image
+class_labels = ['Apel', 'Pisang', 'Paprika', 'Jeruk', 'Wortel', 'Timun']
+
 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
+        return "0", 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}')
+# Upload and predict new images
+uploaded = files.upload()
+results = {}
+for fn in uploaded.keys():
+    file_path = fn
+    jenis_buah, confidence = predict_jenis_buah(combined_model, class_labels, file_path)
+    if jenis_buah == "0":
+        kesegaran_buah = "0"
+    else:
+        kesegaran_buah = predict_kesegaran(combined_model, file_path)
+
+    # Store the results in a dictionary
+    results[fn] = {
+        'Jenis Buah': jenis_buah,
+        'Confidence': float(confidence),
+        'Kesegaran Buah': kesegaran_buah
+    }
+
+    # Optionally, display the image and the predictions
+    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()
 
+# Convert the results to JSON and print/display
+json_output = json.dumps(results, indent=4)
 
 # Menentukan label
 
-        return {"result":predicted_label}
+        return {"result":json_output}
     except Exception as e:
         traceback.print_exc()
         response.status_code = 500