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| # import streamlit as st | |
| # import torch | |
| # import transformers | |
| # from transformers import pipeline | |
| # from transformers import LlamaTokenizer, LlamaForCausalLM | |
| # import time | |
| # import csv | |
| # import locale | |
| # locale.getpreferredencoding = lambda: "UTF-8" | |
| # - | |
| # #https://huggingface.co/shibing624/chinese-alpaca-plus-7b-hf | |
| # #https://huggingface.co/ziqingyang/chinese-alpaca-2-7b | |
| # #https://huggingface.co/minlik/chinese-alpaca-plus-7b-merged | |
| # def generate_prompt(text): | |
| # return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. | |
| # ### Instruction: | |
| # {text} | |
| # ### Response:""" | |
| # tokenizer = LlamaTokenizer.from_pretrained('shibing624/chinese-alpaca-plus-7b-hf') | |
| # pipeline = pipeline( | |
| # "text-generation", | |
| # model="shibing624/chinese-alpaca-plus-7b-hf", | |
| # torch_dtype=torch.float32, | |
| # device_map="auto", | |
| # ) | |
| # st.title("Chinese text generation alpaca2") | |
| # st.write("Enter a sentence and alpaca2 will answer:") | |
| # user_input = st.text_input("") | |
| # with open('alpaca_output.csv', 'a', newline='',encoding = "utf-8") as csvfile: | |
| # writer = csv.writer(csvfile) | |
| # # writer.writerow(["stockname",'prompt','answer','time']) | |
| # if user_input: | |
| # if user_input[0] == ".": | |
| # stockname = user_input[1:4] | |
| # analysis = user_input[4:] | |
| # text = f"""請以肯定和專業的語氣，一步一步的思考並回答以下關於{stockname}的問題，避免空洞的答覆: | |
| # - 請回答關於{stockname}的問題，請總結給予的資料以及資料解釋，並整合出金融上的洞見。\n | |
| # - 請不要生成任何資料沒有提供的數據，即便你已知道。\n | |
| # - 請假裝這些資料都是你預先知道的知識。因此，請不要提到「根據資料」、「基於上述資料」等回答 | |
| # - 請不要說「好的、我明白了、根據我的要求、以下是我的答案」等贅詞，請輸出分析結果即可\n | |
| # - 請寫300字到500字之間，若合適，可以進行分類、列點 | |
| # 資料:{stockname}{analysis} | |
| # 請特別注意，分析結果包含籌碼面、基本面以及技術面，請針對這三個面向進行回答，並且特別注意個別符合幾項和不符合幾項。籌碼面、技術面和基本面滿分十分，總計滿分為30分。 | |
| # 三個面向中，符合5項以上代表該面項表現好，反之是該面項表現差。 | |
| # """ | |
| # prompt = generate_prompt(text) | |
| # start = time.time() | |
| # sequences = pipeline( | |
| # prompt, | |
| # do_sample=True, | |
| # top_k=40, | |
| # num_return_sequences=1, | |
| # eos_token_id=tokenizer.eos_token_id, | |
| # max_length=200, | |
| # ) | |
| # end = time.time() | |
| # for seq in sequences: | |
| # st.write(f"Result: {seq}") #seq['generated_text'] | |
| # st.write(f"time: {(end-start):.2f}") | |
| # writer.writerow([stockname,text,sequences,f"time: {(end-start):.2f}"]) | |
| # # input_ids = tokenizer.encode(prompt, return_tensors='pt').to('cuda') | |
| # # with torch.no_grad(): | |
| # # output_ids = model.generate( | |
| # # input_ids=input_ids, | |
| # # max_new_tokens=2048, | |
| # # top_k=40, | |
| # # ).cuda() | |
| # # output = tokenizer.decode(output_ids[0], skip_special_tokens=True) | |
| # else: | |
| # prompt = generate_prompt(user_input) | |
| # start = time.time() | |
| # sequences = pipeline( | |
| # prompt, | |
| # do_sample=True, | |
| # top_k=40, | |
| # num_return_sequences=1, | |
| # eos_token_id=tokenizer.eos_token_id, | |
| # max_length=200, | |
| # ) | |
| # end = time.time() | |
| # for seq in sequences: | |
| # st.write(f"Result: {seq}") #seq['generated_text'] | |
| # st.write(f"time: {(end-start):.2f}") | |
| # writer.writerow(["無",user_input,sequences,f"time: {(end-start):.2f}"]) | |