gutalk / ap2p.py
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Rename app.py to ap2p.py
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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}"])