app.py

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}"])