Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import gradio as gr
 from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
 
@@ -6,6 +5,7 @@ model_name = "facebook/blenderbot-400M-distill"
 tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
 model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
 
+# declare translate model
 def translate(text,mode):  
     if mode== "ztoe":
         from transformers import AutoModelWithLMHead,AutoTokenizer,pipeline
@@ -21,13 +21,17 @@ def translate(text,mode):
         tokenizer = AutoTokenizer.from_pretrained(mode_name)
         translation = pipeline("translation_en_to_zh", model=model, tokenizer=tokenizer)
         translate_result = translation(text, max_length=400)
-        
     return translate_result
 
+
+#initial list and variable
 chat_history=[]
+output=[]
+bname=''
 
+
+#declare emoji function
 def add_emoji(response):
-    # Define the keywords and their corresponding emojis
     keyword_emoji_dict = {
         "happy": "😀",
         "sad": "😢",
@@ -44,8 +48,36 @@ def add_emoji(response):
         response = response.replace(keyword, f"{keyword} {emoji}")
     return response
 
+
+# Define the keywords and their corresponding keywords
+def tran_shortform(response):
+    keyword_shortform_dict = {
+       "yolo ":  "You only live once",
+        "lol ":"funny" ,
+        "nvm ": "nevermind",
+        "lmk ": "tell me",
+        "btw ": "by the way",
+        "dk ":"don't know",
+        "idk ":"I don't know"   ,  
+        "😀": "happy",
+        "😢": "sad",
+        "😞": "sorry",
+        "❤️": "love",
+        "👍": "like",
+        "👎": "dislike",
+        "🥺": "Why",
+        "🐱": "cat",
+        "🐶": "dog",
+    }
+    response= response.lower()
+    for keyword, st in keyword_shortform_dict.items():
+        response = response.replace(keyword, f"{st}")
+    return response
+
+
+
+# Define the keywords and their corresponding keywords
 def add_shortform(response):
-    # Define the keywords and their corresponding keywords
     keyword_shortform_dict = {
         "You only live once": "YOLO",
         "funny": "LOL",
@@ -55,43 +87,59 @@ def add_shortform(response):
         "tell me": "LMK",
         "By the way": "BTW",
         "don't know":"DK",
-        "do not know":"IDK"    
-    }
+        "do not know":"IDK"  
+        }
+    response= response.lower()
     for keyword, st in keyword_shortform_dict.items():
         response = response.replace(keyword, f"{st}")
     return response
 
+#------------------------------------------------------------------------------------------------------
+#Chatbot start
 def chatbot(text,name):
-    global chat_history
-    global Itext
-    global bname 
-    bname=''
+    global chat_history #for chatbot to response
+    global outputsss # for user to read
+    global bname  #the previous bot name
+    global raw # the text input by user. It will not be changed in the program
+    
+    raw=text #distinguish text for the user to read it(raw) and text for the chatbot (text)
+    
+    # if the user do not name the chatbot. 
     if name=='':
-        name="your chatbot"
+        name="Mr. Chatbot"
+    #since the program will keep running in the website. When the name of chatbot change, the history and chatbot will be refreshed    
     if bname != name:
         chat_history= []
-    bname= name
-    Itext=text
+        outputsss=[]
+        
+    bname=name # same the name     
+    
+    #change the shortform and emoji to english
+    text = tran_shortform(text)    
     
     # Try to detect the language of the input text
-    # If the input language is Chinese, convert the text to lowercase and check if it contains any Chinese characters
     is_chinese = any(0x4e00 <= ord(char) <= 0x9fff for char in text.lower())
+    
+    # If the input language is Chinese, convert the text to lowercase and check if it contains any Chinese characters
     if is_chinese:
         text = translate(text,"ztoe")
         text=f"{text}"
         text=text[23:(len(text)-3)]
+     
             
     # Look for keywords in the previous chat history
     keyword_responses = {
         "how are you": "I'm doing well😄, thank you for asking!",
         "bye": "Goodbye!👊🏻",
+        "bye.": "Goodbye!👊🏻",
         "thank you": "You're welcome!😃",
+        "thank you.": "You're welcome!😃",
         "hello": f'I am {bname}. Nice to meet you!😎',
-        "Hello": f'I am {bname}. Nice to meet you!😎',
-        "Hi": f'I am {bname}. Nice to meet you!😎',
-        "hi": f'I am {bname}. Nice to meet you!😎',   
+        "hello.": f'I am {bname}. Nice to meet you!😎',
+        "hi": f'I am {bname}. Nice to meet you!😎',
     }
-
+  
+        
      # Generate a response based on the previous messages
     if len(chat_history) > 0:
         # Get the last message from the chat history
@@ -107,32 +155,39 @@ def chatbot(text,name):
         generated = model.generate(**encoded_input)
         response = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
         response=f"{response}"
-    if text in keyword_responses:
-            response = keyword_responses[text]
         
 
+
+    # Add emojis to the response
+    response = add_emoji(response)
+    # Add short form to the response
+    response = add_shortform(response)
+    # update history
+    chat_history.append((text,response))
+    
+    # output the standard response 
+    if text.lower() in keyword_responses:
+        response = keyword_responses[text.lower()]  
+            
     # If the input language was Chinese, translate the response back to Chinese
     if is_chinese:
-        from hanziconv import HanziConv
         response = translate(response,"etoz")
+        # remove the header of the output
+        # translate simplified Chinese to traditional Chinese
+        from hanziconv import HanziConv
         response = HanziConv.toTraditional(f"{response}")
         response = f"{response} "
         response=response[23:(len(response)-4)]
+        
+    #if the response is amended. Show the amended text in blanket)
+    if raw.lower() == text.lower():
+        outputsss.append((raw,response))
     else:
-        response = response
-
-    # Add emojis to the response
-    response = add_emoji(response)
-    response = add_shortform(response)
-    chat_history.append((Itext,response))
- 
-    # Format the chat history as an HTML string for display
-    history_str = ""
-    for name, msg in chat_history:
-        history_str += f"<strong>{name}:</strong> {msg}<br>"
-    # Return the response along with the chat history
-    return (chat_history)
+        outputsss.append((f"{raw} ({text})", response))
+        
+    return (outputsss)
 
+#------------------------------------------------------------------------------------------------------
 iface =gr.Interface(fn=chatbot,
     inputs=[gr.inputs.Textbox(label="Chat😃", placeholder="Say somehting💬"),
     gr.inputs.Textbox(label="Name the Bot😺", placeholder="give me a name😉")],
@@ -140,9 +195,9 @@ iface =gr.Interface(fn=chatbot,
     title="🌸Emphatic Chatbot❤️",
     allow_flagging=False,
     layout="vertical",
-    theme='gstaff/xkcd' ,
-    examples=[["再見"], ["Hello"]]
-    )
+    theme='gstaff/xkcd' , # credit: https://huggingface.co/spaces/gstaff/xkcd/tree/main
+    examples=[["你好"], ["Hello"],["I am sad"],["我很傷心"]],
+    article="<H3>Disclaimer</H3><br><oi><li>The chatbot does not have emotions or the ability to empathize in the same way that humans do</li><li>The chatbot can simulate empathy by recognizing and responding to certain emotional cues in language, but responses are generated by algorithms and not based on personal experiences or feelings</li><li>The information and advice provided by the chatbot should not be used as a substitute for professional medical, psychological, or legal adviceUsers should always consult with qualified professionals in these fields for personalized recommendations that take into account their individual circumstances</li><li>The chatbot is not infallible and may encounter technical difficulties or make errors that can impact its performance</li><li>The information presented by the chatbot should always be cross-checked and authenticated before any action is taken based on it</li><li>Interactions with the chatbot may be subject to recording or monitoring for the purposes of quality assurance and training</li><liUser privacy and data protection should always remain a top priority</li><li>Any information collected by the chatbot must be handled in compliance with relevant data protection laws and regulations.</li></oi>")
 #.launch(share=True)
 
 iface.launch()