Create app.py

app.py

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+import gradio as gr
+import onnxruntime as ort
+import json
+import torch
+
+# Load vocab
+with open("vocab.json", "r", encoding="utf-8") as f:
+    vocab = json.load(f)
+inv_vocab = {i:tok for tok,i in vocab.items()}
+
+pad_idx = vocab.get("<pad>", 0)
+unk_idx = vocab.get("<unk>", 1)
+
+# Load ONNX model
+session = ort.InferenceSession("chat_model.onnx")
+
+# Tokenizer helper
+def tokenize(text):
+    return [vocab.get(tok, unk_idx) for tok in text.split(" ")]
+
+def pad_sequence(seq, max_len=20):
+    seq = seq + [pad_idx]*(max_len - len(seq))
+    return seq[:max_len]
+
+# Fungsi chat mini
+def chat_onnx(input_text, max_len=20):
+    input_ids = pad_sequence(tokenize(input_text), max_len)
+    input_tensor = np.array([input_ids], dtype=np.int64)
+    
+    output_ids = []
+    h = None  # ONNX simple RNN ini biasanya stateless
+    for _ in range(max_len):
+        ort_inputs = {"input": input_tensor}
+        ort_outs = session.run(None, ort_inputs)
+        next_token = int(ort_outs[0][0, -1].argmax())
+        output_ids.append(next_token)
+        input_tensor = np.array([[next_token]], dtype=np.int64)
+    
+    return " ".join([inv_vocab.get(i, "<unk>") for i in output_ids])
+
+# Gradio interface
+iface = gr.Interface(fn=chat_onnx, inputs="text", outputs="text")
+iface.launch()