| import os |
| os.environ['CUDA_VISIBLE_DEVICES'] = '-1' |
| os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' |
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' |
| os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" |
|
|
| import tensorflow as tf |
| tf.config.set_visible_devices([], 'GPU') |
|
|
| import gradio as gr |
| import numpy as np |
| from tensorflow.keras.preprocessing import image |
| from tensorflow.keras.models import model_from_json |
| from PIL import Image |
|
|
| from huggingface_hub import hf_hub_download |
|
|
| from description import description |
| from location import location |
|
|
| def load_model_from_file(json_path, h5_path): |
| with open(json_path, "r") as f: |
| json_config = f.read() |
| model = model_from_json(json_config) |
| model.load_weights(h5_path) |
| return model |
|
|
| |
| model_json_path = hf_hub_download(repo_id="CapstoneML/Model", filename="model.json") |
| model_weights_path = hf_hub_download(repo_id="CapstoneML/Model", filename="my_model.h5") |
|
|
| model = load_model_from_file(model_json_path, model_weights_path) |
|
|
| labels = [ |
| "Benteng Vredeburg", "Candi Borobudur", "Candi Prambanan", "Gedung Agung Istana Kepresidenan", |
| "Masjid Gedhe Kauman", "Monumen Serangan 1 Maret", "Museum Gunungapi Merapi", |
| "Situs Ratu Boko", "Taman Sari", "Tugu Yogyakarta" |
| ] |
|
|
| def classify_image(img): |
| try: |
| img = img.resize((224, 224)) |
| img_array = image.img_to_array(img) |
| img_array = np.expand_dims(img_array, axis=0) |
| img_array = img_array / 255.0 |
|
|
| pred = model.predict(img_array)[0] |
| confidence = np.max(pred) |
| predicted_label = labels[np.argmax(pred)] |
|
|
| akurasi = float(confidence) |
| if confidence < 0.8: |
| label_output = "Tidak dapat dikenali (Confidence: {:.2f}%)".format(confidence * 100) |
| deskripsi = ( |
| "Tolong arahkan ke objek yang jelas agar bisa diidentifikasikan. " |
| "Pastikan anda berada di salah satu tempat seperti:\n" |
| "- Benteng Vredeburg\n- Candi Borobudur\n- Candi Prambanan\n" |
| "- Gedung Agung Istana Kepresidenan Yogyakarta\n- Masjid Gedhe Kauman\n" |
| "- Monumen Serangan 1 Maret\n- Museum Gunungapi Merapi\n- Situs Ratu Boko\n" |
| "- Taman Sari\n- Tugu Yogyakarta" |
| ) |
| lokasi = "-" |
| else: |
| label_output = f"{predicted_label} (Confidence: {confidence * 100:.2f}%)" |
| deskripsi = description.get(predicted_label, "Deskripsi belum tersedia.") |
| lokasi = location.get(predicted_label, None) |
| if lokasi: |
| lokasi = f'<a href="{lokasi}" target="_blank">Lihat Lokasi di Google Maps</a>' |
| else: |
| lokasi = "Lokasi tidak ditemukan" |
|
|
| return label_output, deskripsi, lokasi, akurasi |
|
|
| except Exception as e: |
| return "Error", str(e), "-" |
|
|
| interface = gr.Interface( |
| fn=classify_image, |
| inputs=gr.Image(type="pil", label="Upload Gambar"), |
| outputs=[ |
| gr.Textbox(label="Output Klasifikasi"), |
| gr.Textbox(label="Deskripsi Lengkap", lines=20, max_lines=50), |
| gr.HTML(label="Link Lokasi"), |
| ], |
| title="Klasifikasi Gambar", |
| description="Upload gambar, sistem akan mengklasifikasikan dan memberikan deskripsi mengenai gambar tersebut." |
| ) |
|
|
| interface.launch() |
|
|
