'product-407-1'

.gitignore

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+__pycache__

README copy.md

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+---
+title: Chatbot Smartid Apps
+emoji: 💬
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 5.0.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: (Bi-LSTM Encoder + LSTM Decoder + Multiplicative Attention)
+---
+
+An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

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+from templates import demo
+
+if __name__ == "__main__":
+    demo.launch()

models/best_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f40918ad8d7c870738ea912267df1b45a4e4b12715b59624ab4043deeb0909d1
+size 32753848

models/tokenizer_input.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a5b3843c10879971954f83a50fa1026c482d337806fa8d60423db8c8fd1a1cbe
+size 2900

models/tokenizer_target.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4b34ae5502ddfaebfc273aba8af102c9807cf5ee91aeb601dd0d2aaf4396dc4b
+size 1961

requirements.txt

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+absl-py==2.3.1
+aiofiles==23.2.1
+altair==5.5.0
+annotated-types==0.7.0
+anyio==4.9.0
+astunparse==1.6.3
+attrs==25.3.0
+bert-score==0.3.13
+certifi==2025.6.15
+charset-normalizer==3.4.2
+click==8.2.1
+colorama==0.4.6
+contourpy==1.3.2
+cycler==0.12.1
+fastapi==0.115.14
+ffmpy==0.6.0
+filelock==3.18.0
+flatbuffers==25.2.10
+fonttools==4.58.5
+fsspec==2025.5.1
+gast==0.6.0
+google-pasta==0.2.0
+gradio==3.50.2
+gradio_client==0.6.1
+groovy==0.1.2
+grpcio==1.73.1
+h11==0.16.0
+h5py==3.14.0
+httpcore==1.0.9
+httpx==0.28.1
+huggingface-hub==0.33.2
+idna==3.10
+importlib_resources==6.5.2
+Jinja2==3.1.6
+joblib==1.5.1
+jsonschema==4.24.0
+jsonschema-specifications==2025.4.1
+keras==3.10.0
+kiwisolver==1.4.8
+libclang==18.1.1
+Markdown==3.8.2
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.10.3
+mdurl==0.1.2
+ml_dtypes==0.5.1
+mpmath==1.3.0
+namex==0.1.0
+narwhals==1.45.0
+nltk==3.9.1
+numpy==1.26.4
+opt_einsum==3.4.0
+optree==0.16.0
+orjson==3.10.18
+packaging==25.0
+pandas==2.3.0
+pillow==10.4.0
+protobuf==5.29.5
+pydantic==2.11.7
+pydantic_core==2.33.2
+pydub==0.25.1
+Pygments==2.19.2
+pyparsing==3.2.3
+python-dateutil==2.9.0.post0
+python-multipart==0.0.20
+pytz==2025.2
+PyYAML==6.0.2
+referencing==0.36.2
+regex==2024.11.6
+requests==2.32.4
+rich==14.0.0
+rpds-py==0.26.0
+ruff==0.12.2
+safehttpx==0.1.6
+safetensors==0.5.3
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.17.0
+sniffio==1.3.1
+starlette==0.46.2
+sympy==1.14.0
+tensorboard==2.19.0
+tensorboard-data-server==0.7.2
+tensorflow==2.19.0
+tensorflow-io-gcs-filesystem==0.31.0
+termcolor==3.1.0
+tokenizers==0.21.2
+tomlkit==0.13.3
+torch==2.7.1
+tqdm==4.67.1
+transformers==4.53.0
+typer==0.16.0
+typing-inspection==0.4.1
+typing_extensions==4.14.0
+tzdata==2025.2
+urllib3==2.5.0
+uvicorn==0.35.0
+websockets==11.0.3
+Werkzeug==3.1.3
+wrapt==1.17.2

templates.py

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+import gradio as gr
+from utils import chatbot as generate_response
+
+demo = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(label="Pertanyaan dan keluhan", 
+                   placeholder="Berikan pertanyaan/keluhan anda terhadap sistem"),
+    ],
+    outputs="text",
+    title="Customer Service AI Smart ID",
+    description=(
+        "Pertanyaan dan keluhan anda akan dijawab oleh mas Smarter"
+    )
+)

utils.py

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+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import load_model
+import pickle
+import numpy as np
+
+# model yang ingin dimuat
+try:
+    model = load_model("models/best_model.h5", compile=True)
+    with open("models/tokenizer_input.pkl", 'rb') as f:
+        tokenizer_inputs = pickle.load(f)
+    with open("models/tokenizer_target.pkl", 'rb') as f:
+        tokenizer_outputs = pickle.load(f)
+except Exception as e:
+    print(f"Error loading model: {e}")
+    raise
+
+# kalkulasi sample temperature agar lebih hangat generatif nya
+def sample_with_temperature(probs, temperature=1.0, top_k=None):
+    if temperature != 1.0:
+        probs = np.log(probs) / temperature
+        probs = np.exp(probs)
+        probs = probs / np.sum(probs)
+    
+    if top_k is not None:
+        top_k_indices = np.argpartition(probs, -top_k)[-top_k:]
+        top_k_probs = probs[top_k_indices]
+        top_k_probs = top_k_probs / np.sum(top_k_probs)  # Renormalize
+        sampled_index = np.random.choice(top_k_indices, p=top_k_probs)
+    else:
+        sampled_index = np.random.choice(len(probs), p=probs)
+    
+    return sampled_index
+
+# fungsi prediksi teks
+def predict_with_main_model(user_text, tokenizer_input, tokenizer_target, model,
+                            max_len=15, temperature=1.0, top_k=None, max_encoder_len=9, max_decoder_len=15):
+    if max_len is None:
+        max_len = max_decoder_len  
+
+    input_seq = tokenizer_input.texts_to_sequences([user_text])
+    encoder_input = pad_sequences(input_seq, maxlen=max_encoder_len, padding='post')
+
+    start_token = tokenizer_target.word_index.get('<sos>', 1)
+    end_token = tokenizer_target.word_index.get('<eos>', 2)
+
+    decoder_input = np.zeros((1, max_len - 1), dtype='int32')
+    decoder_input[0, 0] = start_token
+
+    decoded_tokens = []
+
+    for i in range(1, max_len - 1):
+        predictions = model.predict([encoder_input, decoder_input], verbose=0)
+        token_probs = predictions[0, i - 1]
+
+        if top_k:
+            token_id = sample_with_temperature(token_probs, temperature, top_k)
+        else:
+            token_id = np.argmax(token_probs)
+
+        if token_id == end_token:
+            break
+
+        word = tokenizer_target.index_word.get(token_id, '')
+        if word and word != '<sos>':
+            decoded_tokens.append(word)
+
+        decoder_input[0, i] = token_id
+    return ' '.join(decoded_tokens)
+
+# ada penambahan riwayat pesan (memori)
+def chatbot(user_message):
+    response = predict_with_main_model(
+        user_message,
+        tokenizer_inputs,
+        tokenizer_outputs,
+        model,
+        temperature=1.0,
+        top_k=10
+    )
+
+    return response