fyp

app.py

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+
+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)
+
+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
+
+chat_history=[]
+
+def add_emoji(response):
+    # Define the keywords and their corresponding emojis
+    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
+
+def add_shortform(response):
+    # Define the keywords and their corresponding keywords
+    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",
+        "do not know":"IDK"    
+    }
+    for keyword, st in keyword_shortform_dict.items():
+        response = response.replace(keyword, f"{st}")
+    return response
+
+def chatbot(text,name):
+    global chat_history
+    global Itext
+    global bname 
+    if name=='':
+        name="your chatbot"
+    bname= name
+    Itext=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 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!👊🏻",
+        "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!😎',   
+    }
+
+     # 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}"
+    if text in keyword_responses:
+            response = keyword_responses[text]
+        
+
+    # If the input language was Chinese, translate the response back to Chinese
+    if is_chinese:
+        from hanziconv import HanziConv
+        response = translate(response,"etoz")
+        response = HanziConv.toTraditional(f"{response}")
+        response = f"{response} "
+        response=response[23:(len(response)-4)]
+    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)
+
+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' ,
+    examples=[["再見"], ["Hello"]]
+    )
+#.launch(share=True)
+
+iface.launch(share=True)