app.py

import gradio as gr
from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration

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
        mode_name = 'liam168/trans-opus-mt-zh-en'
        model = AutoModelWithLMHead.from_pretrained(mode_name)
        tokenizer = AutoTokenizer.from_pretrained(mode_name)
        translation = pipeline("translation_zh_to_en", model=model, tokenizer=tokenizer)
        translate_result = translation(text, max_length=400)
    if mode ==  "etoz":
        from transformers import AutoModelWithLMHead,AutoTokenizer,pipeline
        mode_name = 'liam168/trans-opus-mt-en-zh'
        model = AutoModelWithLMHead.from_pretrained(mode_name)
        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):
    keyword_emoji_dict = {
        "happy": "😀",
        "sad": "😢",
        "sorry":"😞",
        "love": "❤️",
        "like": "👍",
        "dislike": "👎",
        "Why": "🥺",
        "cat":"🐱",
        "dog":"🐶",
        "嗨" :  "😎"
    }
    for keyword, emoji in keyword_emoji_dict.items():
        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):
    keyword_shortform_dict = {
        "You only live once": "YOLO",
        "funny": "LOL",
        "laugh":"LOL",
        "nevermind": "nvm",
        "sorry": "sorryyyyy",
        "tell me": "LMK",
        "By the way": "BTW",
        "don't know":"DK",
        "i don't 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 #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="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= []
        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
    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!😎',
    }
  
        
     # Generate a response based on the previous messages
    if len(chat_history) > 0:
        # Get the last message from the chat history
        last_message = chat_history[-1][1]
        # Generate a response based on the last message
        encoded_input = tokenizer.encode(last_message + tokenizer.eos_token + text, return_tensors='pt')
        generated = model.generate(encoded_input, max_length=1024, do_sample=True)
        response = tokenizer.decode(generated[0], skip_special_tokens=True)
        response=f"{response}"
    else:
        # If there is no previous message, generate a response using the default method
        encoded_input = tokenizer(text, return_tensors='pt')
        generated = model.generate(**encoded_input)
        response = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
        response=f"{response}"
        

    
    # Add emojis to the response
    response = add_emoji(response)
    # Add short form to the response
    response = add_shortform(response)
    
    
    # output the standard response 
    if text.lower() in keyword_responses:
        response = keyword_responses[text.lower()]  
        
        # update history
    chat_history.append((text,response))
            
    # If the input language was Chinese, translate the response back to Chinese
    if is_chinese:
        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:
        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😉")],
    outputs=[gr.Chatbot(label="Chat Here☺️")], 
    title="🌸Emphatic Chatbot❤️",
    allow_flagging=False,
    layout="vertical",
    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()