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