Create app.py

app.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}"])