first update chinese alpaca2

app.py

@@ -1,16 +1,78 @@
 import streamlit as st
+import torch
+import transformers
 from transformers import pipeline
+from transformers import LlamaTokenizer, LlamaForCausalLM
+import time
 
-sentiment_pipeline = pipeline("sentiment-analysis")
+#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
 
-st.title("Sentiment Analysis with HuggingFace Spaces")
-st.write("Enter a sentence to analyze its sentiment:")
+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.float16,
+    device_map="auto",
+)
+
+st.title("Chinese text generation alpaca2")
+st.write("Enter a sentence and alpaca2 will answer:")
 
 user_input = st.text_input("")
-if user_input:
-    result = sentiment_pipeline(user_input)
-    sentiment = result[0]["label"]
-    confidence = result[0]["score"]
 
-    st.write(f"Sentiment: {sentiment}")
-    st.write(f"Confidence: {confidence:.2f}")
+
+
+
+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:
+        stockname = user_input[0:3]
+        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}")
+    
+        # 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)