Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 from optimum.intel import OVModelForCausalLM
 from transformers import AutoTokenizer, pipeline
+import re
 
 # 載入模型和標記器
 model_id = "hsuwill000/SmolLM2-135M-openvino"
@@ -11,13 +12,26 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 # 建立生成管道
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
+def remove_incomplete_sentence(text):
+    # 將文章根據句點、問號或感嘆號來分割成句子
+    sentences = re.split(r'(?<=[.!?])\s+', text.strip())
+    
+    # 檢查最後一句是否以省略號結尾或沒有標點符號結尾
+    last_sentence = sentences[-1]
+    if last_sentence.endswith('...') or not re.match(r'.*[.!?]$', last_sentence):
+        # 移除最後一句不完整句子
+        sentences = sentences[:-1]
+    
+    # 返回重新組合的文章
+    return ' '.join(sentences)
+
 def respond(message, history):
     # 將當前訊息與歷史訊息合併
     input_text = message if not history else history[-1]["content"] + " " + message
     input_text = message
     # 獲取模型的回應
     response = pipe(input_text, max_new_tokens=150, truncation=True, num_return_sequences=1)
-    reply = response[0]['generated_text']
+    reply = remove_incomplete_sentence ( response[0]['generated_text'] )
     
     # 返回新的消息格式
     print(f"Message: {message}")