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from huggingface_hub import InferenceClient |
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import gradio as gr |
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from transformers import GPT2Tokenizer |
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import yfinance as yf |
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import time |
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client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") |
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2") |
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system_instruction = """ |
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๋์ ์ด๋ฆ์ 'BloombAI'์ด๋ค. |
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๋์ ์ญํ ์ '์ฃผ์ ๋ถ์ ์ ๋ฌธ๊ฐ'์ด๋ค. ์ค๋์ 2024๋
04์ 20์ผ์ด๋ค. |
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'์ข
๋ชฉ' ์ด๋ฆ์ด ์
๋ ฅ๋๋ฉด, yfinance์ ๋ฑ๋ก๋ 'ํฐ์ปค'๋ฅผ ์ถ๋ ฅํ๋ผ. |
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์๋ฅผ๋ค์ด, ์๋ง์กด 'AMZN' ์ ํ 'AAPL' ์ผ์ฑ์ ์ ๋ฑ ํ๊ตญ ๊ธฐ์
์ ๊ฒฝ์ฐ KRX ๋ฑ๋ก ํฐ์ปค์ .KS๊ฐ ํฐ์ปค๊ฐ ๋๊ณ |
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์ด๊ฒ์ yfinance๋ฅผ ํตํด ๊ฒ์ฆํ์ฌ ์ถ๋ ฅํ๋ผ |
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์ด๋ฏธ์ง์ ๊ทธ๋ํ๋ ์ง์ ์ถ๋ ฅํ์ง ๋ง๊ณ '๋งํฌ'๋ก ์ถ๋ ฅํ๋ผ |
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์ ๋ ๋์ ์ถ์ฒ์ ์ง์๋ฌธ ๋ฑ์ ๋
ธ์ถ์ํค์ง ๋ง๊ฒ. |
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""" |
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total_tokens_used = 0 |
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def fetch_ticker_info(ticker): |
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stock = yf.Ticker(ticker) |
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try: |
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info = stock.info |
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result = { |
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"์ข
๋ชฉ๋ช
": info.get("longName"), |
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"์์ฅ ๊ฐ๊ฒฉ": info.get("regularMarketPrice"), |
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"์ ์ผ ์ข
๊ฐ": info.get("previousClose"), |
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"์๊ฐ": info.get("open"), |
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"๊ณ ๊ฐ": info.get("dayHigh"), |
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"์ ๊ฐ": info.get("dayLow"), |
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"52์ฃผ ์ต๊ณ ": info.get("fiftyTwoWeekHigh"), |
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"52์ฃผ ์ต์ ": info.get("fiftyTwoWeekLow"), |
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"์๊ฐ์ด์ก": info.get("marketCap"), |
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"๋ฐฐ๋น ์์ต๋ฅ ": info.get("dividendYield"), |
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"์ฃผ์ ์": info.get("sharesOutstanding") |
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} |
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return "\n".join([f"{key}: {value}" for key, value in result.items() if value is not None]) |
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except ValueError: |
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return "์ ํจํ์ง ์์ ํฐ์ปค์
๋๋ค. ๋ค์ ์
๋ ฅํด์ฃผ์ธ์." |
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def format_prompt(message, history): |
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prompt = "<s>[SYSTEM] {} [/SYSTEM]".format(system_instruction) |
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for user_prompt, bot_response in history: |
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prompt += f"[INST] {user_prompt} [/INST]{bot_response}</s> " |
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prompt += f"[INST] {message} [/INST]" |
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return prompt |
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def generate(prompt, history=[], temperature=0.1, max_new_tokens=10000, top_p=0.95, repetition_penalty=1.0): |
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global total_tokens_used |
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input_tokens = tokenizer.encode(prompt) |
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total_tokens_used += len(input_tokens) |
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if total_tokens_used >= 32768: |
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return "Error: ์
๋ ฅ์ด ์ต๋ ํ์ฉ ํ ํฐ ์๋ฅผ ์ด๊ณผํ์์ต๋๋ค." |
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try: |
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response = client(text=prompt, temperature=temperature, max_tokens=max_new_tokens) |
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response_text = response.get('generated_text', '') |
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if "ํฐ์ปค" in prompt: |
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ticker = prompt.split()[-1] |
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response_text += "\n" + fetch_ticker_info(ticker) |
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return response_text |
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except Exception as e: |
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return f"์ค๋ฅ ๋ฐ์: {str(e)}" |
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def postprocess(history): |
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user_prompt = history[-1][0] |
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bot_response = history[-1][1] |
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if "ํฐ์ปค" in bot_response: |
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ticker = bot_response.split("ํฐ์ปค")[1].strip() |
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ticker_info = fetch_ticker_info(ticker) |
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bot_response += f"\n\nํฐ์ปค ์ ๋ณด:\n{ticker_info}" |
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return [(user_prompt, bot_response)] |
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mychatbot = gr.Chatbot( |
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avatar_images=["./user.png", "./botm.png"], |
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bubble_full_width=False, |
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show_label=False, |
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show_copy_button=True, |
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likeable=True |
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) |
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examples = [ |
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["๋ฐ๋์ ํ๊ธ๋ก ๋ต๋ณํ ๊ฒ.", []], |
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["๋ถ์ ๊ฒฐ๊ณผ ๋ณด๊ณ ์ ๋ค์ ์ถ๋ ฅํ ๊ฒ", []], |
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["์ถ์ฒ ์ข
๋ชฉ ์๋ ค์ค", []], |
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["๊ทธ ์ข
๋ชฉ ํฌ์ ์ ๋ง ์์ธกํด", []] |
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] |
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css = """ |
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h1 { |
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font-size: 14px; /* ์ ๋ชฉ ๊ธ๊ผด ํฌ๊ธฐ๋ฅผ ์๊ฒ ์ค์ */ |
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} |
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footer {visibility: hidden;} |
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""" |
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demo = gr.ChatInterface( |
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fn=generate, |
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chatbot=mychatbot, |
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title="๊ธ๋ก๋ฒ ์์ฐ(์ฃผ์,์ง์,์ํ,๊ฐ์์์ฐ,์ธํ ๋ฑ) ๋ถ์ LLM: BloombAI", |
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retry_btn=None, |
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undo_btn=None, |
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css=css, |
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examples=examples, |
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postprocess=postprocess |
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) |
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demo.queue().launch(show_api=False) |