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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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model_name = "deepseek-ai/deepseek-coder-1.3b-base" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto") |
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def generate_code(prompt, max_tokens): |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=max_tokens, temperature=0.7) |
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generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return generated_code[len(prompt):] |
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with gr.Blocks() as demo: |
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gr.Markdown("# π DeepSeek Code Generator") |
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with gr.Row(): |
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prompt_input = gr.Textbox(label="Masukkan prompt (instruksi)", placeholder="Contoh: Buatkan fungsi Python untuk menghitung faktorial") |
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token_slider = gr.Slider(32, 1024, value=256, label="Jumlah token maksimum") |
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with gr.Row(): |
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generate_button = gr.Button("π Generate Code") |
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output = gr.Code(label="Output Kode") |
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generate_button.click(fn=generate_code, inputs=[prompt_input, token_slider], outputs=output) |
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demo.launch() |
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