Update app.py

app.py

@@ -1,106 +1,106 @@
-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"
+# 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
+# #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}
+# 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:"""
+#     ### 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",
-)
+# 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:")
+# st.title("Chinese text generation alpaca2")
+# st.write("Enter a sentence and alpaca2 will answer:")
 
-user_input = st.text_input("")
+# 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:]
+# 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}
+#             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。
-            """
+#             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}"])
+#             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,
+#             # 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}"])
+#             #     ).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}"])