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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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from threading import Thread |
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import gradio as gr |
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class ChatbotService: |
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def __init__(self, model_name="RajuKandasamy/tamillama_tiny_30m"): |
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self.model = AutoModelForCausalLM.from_pretrained(model_name) |
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self.tokenizer = AutoTokenizer.from_pretrained(model_name) |
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self.streamer = None |
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def call(self, prompt): |
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self.streamer = TextIteratorStreamer(self.tokenizer, skip_prompt=True, timeout=5) |
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prompt = prompt.replace("<br>", "\n") |
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print(prompt) |
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inputs = self.tokenizer(prompt, return_tensors="pt") |
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print(inputs) |
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kwargs = dict(input_ids=inputs["input_ids"], streamer=self.streamer, max_new_tokens=512, do_sample=True, top_p=0.8, top_k=500, temperature=0.001, repetition_penalty=1.4) |
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thread = Thread(target=self.model.generate, kwargs=kwargs) |
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thread.start() |
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return "" |
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import gradio as gr |
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example_questions = [ |
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f"""சொற்கள்: |
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வீழ்ச்சி, சீட்டு, பிடிவாதம் |
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சுருக்கம்:""", |
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f"""சொற்கள்: |
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ஓட்டம், பயணம், குழப்பம் |
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சுருக்கம்:""", |
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f"""Words: prevent, car, broken |
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Features: Dialogue""", |
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f"""சொற்கள்: |
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திரும்பு, வாசனை திரவியம், துணிச்சல் |
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சுருக்கம்:""" |
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] |
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chatbot_service = ChatbotService() |
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with gr.Blocks() as demo: |
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chatbot = gr.Chatbot() |
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with gr.Row(): |
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msg = gr.Textbox(placeholder="Type your message here...", inputs="text",outputs="text", label="Story Prompt:") |
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run = gr.Button("Run") |
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examples_dropdown = gr.Dropdown(choices=example_questions, label="Select an example prompt") |
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examples_dropdown.change(fn=lambda x: x, inputs=examples_dropdown, outputs=msg) |
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clear = gr.Button("Clear") |
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def user(question, user_message, history): |
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if history == None: |
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history = [] |
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user_message = question |
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return "", history + [[user_message, None]] |
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def bot(history): |
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chatbot_service.call(history[-1][0]) |
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history[-1][1] = "" |
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for character in chatbot_service.streamer: |
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print(character) |
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history[-1][1] += character |
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yield history |
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run.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, chatbot, chatbot) |
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clear.click(lambda: None, None, chatbot, queue=False) |
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demo.queue() |
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demo.launch() |
