fyp
Browse files
app.py
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import gradio as gr
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from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
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model_name = "facebook/blenderbot-400M-distill"
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tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
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model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
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def translate(text,mode):
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if mode== "ztoe":
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from transformers import AutoModelWithLMHead,AutoTokenizer,pipeline
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mode_name = 'liam168/trans-opus-mt-zh-en'
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model = AutoModelWithLMHead.from_pretrained(mode_name)
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tokenizer = AutoTokenizer.from_pretrained(mode_name)
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translation = pipeline("translation_zh_to_en", model=model, tokenizer=tokenizer)
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translate_result = translation(text, max_length=400)
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if mode == "etoz":
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from transformers import AutoModelWithLMHead,AutoTokenizer,pipeline
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mode_name = 'liam168/trans-opus-mt-en-zh'
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model = AutoModelWithLMHead.from_pretrained(mode_name)
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tokenizer = AutoTokenizer.from_pretrained(mode_name)
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translation = pipeline("translation_en_to_zh", model=model, tokenizer=tokenizer)
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translate_result = translation(text, max_length=400)
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return translate_result
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chat_history=[]
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def add_emoji(response):
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# Define the keywords and their corresponding emojis
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keyword_emoji_dict = {
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"happy": "π",
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"sad": "π’",
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"sorry":"π",
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"love": "β€οΈ",
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"like": "π",
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"dislike": "π",
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"Why": "π₯Ί",
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"cat":"π±",
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"dog":"πΆ",
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"ε¨" : "π"
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}
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for keyword, emoji in keyword_emoji_dict.items():
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response = response.replace(keyword, f"{keyword} {emoji}")
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return response
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def add_shortform(response):
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# Define the keywords and their corresponding keywords
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keyword_shortform_dict = {
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"You only live once": "YOLO",
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"funny": "LOL",
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"laugh":"LOL",
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"nevermind": "nvm",
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"sorry": "sorryyyyy",
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"tell me": "LMK",
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"By the way": "BTW",
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"don't know":"DK",
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"do not know":"IDK"
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}
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for keyword, st in keyword_shortform_dict.items():
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response = response.replace(keyword, f"{st}")
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return response
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def chatbot(text,name):
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global chat_history
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global Itext
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global bname
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if name=='':
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name="your chatbot"
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bname= name
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Itext=text
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# Try to detect the language of the input text
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# If the input language is Chinese, convert the text to lowercase and check if it contains any Chinese characters
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is_chinese = any(0x4e00 <= ord(char) <= 0x9fff for char in text.lower())
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if is_chinese:
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text = translate(text,"ztoe")
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text=f"{text}"
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text=text[23:(len(text)-3)]
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# Look for keywords in the previous chat history
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keyword_responses = {
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"how are you": "I'm doing wellπ, thank you for asking!",
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"bye": "Goodbye!ππ»",
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"thank you": "You're welcome!π",
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"hello": f'I am {bname}. Nice to meet you!π',
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"Hello": f'I am {bname}. Nice to meet you!π',
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"Hi": f'I am {bname}. Nice to meet you!π',
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"hi": f'I am {bname}. Nice to meet you!π',
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}
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# Generate a response based on the previous messages
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if len(chat_history) > 0:
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# Get the last message from the chat history
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last_message = chat_history[-1][1]
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# Generate a response based on the last message
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encoded_input = tokenizer.encode(last_message + tokenizer.eos_token + text, return_tensors='pt')
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generated = model.generate(encoded_input, max_length=1024, do_sample=True)
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response = tokenizer.decode(generated[0], skip_special_tokens=True)
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response=f"{response}"
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else:
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# If there is no previous message, generate a response using the default method
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encoded_input = tokenizer(text, return_tensors='pt')
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generated = model.generate(**encoded_input)
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response = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
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response=f"{response}"
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if text in keyword_responses:
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response = keyword_responses[text]
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# If the input language was Chinese, translate the response back to Chinese
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if is_chinese:
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from hanziconv import HanziConv
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response = translate(response,"etoz")
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response = HanziConv.toTraditional(f"{response}")
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response = f"{response} "
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response=response[23:(len(response)-4)]
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else:
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response = response
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# Add emojis to the response
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response = add_emoji(response)
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response = add_shortform(response)
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chat_history.append((Itext,response))
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# Format the chat history as an HTML string for display
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history_str = ""
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for name, msg in chat_history:
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history_str += f"<strong>{name}:</strong> {msg}<br>"
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# Return the response along with the chat history
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return (chat_history)
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iface =gr.Interface(fn=chatbot,
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inputs=[gr.inputs.Textbox(label="Chat", placeholder="Say somehting"),
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gr.inputs.Textbox(label="Name the Bot", placeholder="give me a name")],
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outputs=[gr.Chatbot(label="Chat Here")],
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title="Emphatic Chatbot",
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allow_flagging=False,
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layout="vertical",
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theme='gstaff/xkcd' ,
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examples=[["εθ¦"], ["Hello"]]
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)
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#.launch(share=True)
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iface.launch(share=True)
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