updated prompt

app.py

@@ -2,7 +2,6 @@
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from langchain.vectorstores import Chroma
 from langchain.embeddings import HuggingFaceEmbeddings
-from peft import AutoPeftModelForCausalLM
 
 import gradio as gr
 import hanzidentifier
@@ -18,14 +17,15 @@ current_path=str(pathlib.Path(__file__).parent.resolve())
 llm_model_name="Qwen/Qwen1.5-0.5B-Chat"
 
 #pipe = pipeline("text2text-generation", model=model)
-#model = AutoModelForCausalLM.from_pretrained(
-#    llm_model_name
-#)
-
 model = AutoModelForCausalLM.from_pretrained(
     "ytyeung/Qwen1.5-0.5B-Chat-SFT-riddles",
 )
 
+#model = AutoPeftModelForCausalLM.from_pretrained(
+#    "Qwen1.5_0.5B_Chat_sft_full/checkpoint-300",
+#    low_cpu_mem_usage=True,
+#)
+
 tokenizer = AutoTokenizer.from_pretrained(llm_model_name)
 
 # %%
@@ -72,16 +72,20 @@ threshold = 0.6
 def answer(input_text,context=None):
     if context:
         tips = "提示：\n"
-        tips += "\n".join([x[0] for x in context])
-        print (f"====\n{input_text}\n{context[0][0]} {context[0][1]}")
-        if context[0][1] >=0.9:
-            ans = re.search(r"谜底：(\w+)", context[0][0])
-            if ans:
-                return f"谜底是：{ans.group(1)}"
+        for i, tip in enumerate(context):
+            #if i==0:
+            #    tips +="最佳答案\n"
+            #else:
+            #    tips +="較差答案\n"
+            tips += f"谜面：{tip[0]} 谜底是：{tip[1]} "
+            tips +="\n"
+        print (f"====\n{input_text}\n{context[0][0]} 谜底是：{context[0][1]} {context[0][2]}")
+        if context[0][2] >=0.9:
+            return f"谜底是：{context[0][1]}"
     else:
         tips=""
     
-    prompt = f"{input_text}\n{tips}\n谜底是什么？"
+    prompt = f"{input_text}\n\n{tips}\n\n谜底是什么？"
     prompt = prompt.strip()
 
     print(f"===\n{prompt}")
@@ -120,7 +124,7 @@ def helper_rag(text):
     context = []
     for doc in docs_out:
         if doc[1] > threshold:
-            context.append((f"{doc[0].page_content}{doc[0].metadata['answer']}", doc[1]))
+            context.append((doc[0].page_content, doc[0].metadata['answer'], doc[1]))
 
     return context
 
@@ -132,7 +136,7 @@ def helper_text(text_input,radio=None):
         text_input = chinese_converter.to_simplified(text_input)
         chinese_type = "traditional"
 
-    text_input = re.sub(r'hint',"猜",text_input,flags=re.I)
+    text_input = re.sub(r'hint:',"猜",text_input,flags=re.I)
 
     #if not any(c in text_input for c in ["猜", "打"]):
     #    warning = "请给一个提示，提示格式，例子：猜一水果，打一字。"
@@ -140,10 +144,9 @@ def helper_text(text_input,radio=None):
     #        warning = chinese_converter.to_traditional(warning)
     #    return warning
                                          
-    text=f"""猜谜语：\n谜面：{text_input}
-    """
+    text=f"""猜谜语：\n谜面：{text_input}"""
 
-    context = helper_rag(text)
+    context = helper_rag(text_input)
 
     output = answer(text,context=context)
 
@@ -248,6 +251,7 @@ def translate_text(text_input,text_output):
 
     text_input = translate(f"{text_input}")
     text_output = translate(f"{text_output}")
+
     return text_input,text_output
     
 
@@ -266,7 +270,7 @@ with gr.Blocks() as demo:
         ["小家伙穿黄袍，花丛中把房造。飞到西来飞到东，人人夸他爱劳动。（猜一动物）"],
         ["一物生来身穿三百多件衣，每天脱一件，年底剩张皮。（猜一物品）"],
         ["A thousand threads, a million strands. Reaching the water, vanishing all at once. (Hint: natural phenomenon)"],
-        ["无底洞"], 
+        ["无底洞（猜一成语）"], 
     ]
     radio = gr.Radio(
         ["简体中文","繁體中文", "English"],show_label=False,value="简体中文"

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