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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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import torch
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from threading import Thread
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MODEL_ID = "uncensoredai/UncensoredLM-DeepSeek-R1-Distill-Qwen-14B"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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def generate_text(prompt, temperature=0.8, top_p=0.9, max_new_tokens=512):
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True)
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generation_kwargs = dict(
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**inputs,
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streamer=streamer,
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temperature=temperature,
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top_p=top_p,
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do_sample=True,
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max_new_tokens=max_new_tokens
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)
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thread = Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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output = ""
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for new_text in streamer:
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output += new_text
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yield output
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with gr.Blocks(title="Uncensored DeepSeek Qwen 14B") as demo:
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gr.Markdown("## 🧠 Uncensored DeepSeek Qwen 14B")
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gr.Markdown("Thai & English Chatbot – Powered by Qwen 14B Distilled Model")
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with gr.Row():
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with gr.Column(scale=3):
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prompt = gr.Textbox(label="Input", placeholder="พิมพ์ข้อความที่นี่...", lines=3)
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temperature = gr.Slider(0.1, 1.5, value=0.8, step=0.1, label="Temperature")
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top_p = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top P")
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max_new_tokens = gr.Slider(64, 2048, value=512, step=64, label="Max New Tokens")
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btn = gr.Button("Generate")
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with gr.Column(scale=5):
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output = gr.Textbox(label="AI Response", lines=20)
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btn.click(generate_text, inputs=[prompt, temperature, top_p, max_new_tokens], outputs=output)
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demo.queue().launch()
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