app.py

from transformers import BertTokenizerFast,TFBertForSequenceClassification,TextClassificationPipeline
import numpy as np
import tensorflow as tf
import gradio as gr 
import openai
import os

# Sentiment Analysis Pre-Trained Model
model_path = "leadingbridge/sentiment-analysis"
tokenizer = BertTokenizerFast.from_pretrained(model_path)
model = TFBertForSequenceClassification.from_pretrained(model_path, id2label={0: 'negative', 1: 'positive'} )

def sentiment_analysis(text):
  pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
  result = pipe(text)
  return result


# Open AI Chatbot Model
openai.api_key = "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"

start_sequence = "\nAI:"
restart_sequence = "\nHuman: "

prompt = "You can discuss any topic with the Chinese Chatbot assistant by typing Chinese in here"

def openai_create(prompt):

    response = openai.Completion.create(
    model="text-davinci-003",
    prompt=prompt,
    temperature=0.9,
    max_tokens=1024,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0.6,
    stop=[" Human:", " AI:"]
    )

    return response.choices[0].text

def chatgpt_clone(input, history):
    history = history or []
    s = list(sum(history, ()))
    s.append(input)
    inp = ' '.join(s)
    output = openai_create(inp)
    history.append((input, output))
    return history, history


# Open AI Chinese Translation Model
def translate_to_chinese(text_to_translate):
    response = openai.Completion.create(
      model="text-davinci-003",
      prompt=f"Translate this short English sentence into Chinese:\n\n{text_to_translate}\n\n1.",
      temperature=0.3,
      max_tokens=1024,
      top_p=1.0,
      frequency_penalty=0.0,
      presence_penalty=0.0
    )
    return response.choices[0].text.strip()

# Open AI English Translation Model
def translate_to_english(text_to_translate):
    response = openai.Completion.create(
      model="text-davinci-003",
      prompt=f"Translate this short Chinese sentence into English:\n\n{text_to_translate}\n\n1.",
      temperature=0.3,
      max_tokens=1024,
      top_p=1.0,
      frequency_penalty=0.0,
      presence_penalty=0.0
    )
    return response.choices[0].text.strip()


# Gradio Output Model
with gr.Blocks() as demo:
    gr.Markdown("Welcome to the Chinese NLP demo! Please select a model tab to interact with: ")
    with gr.Tab("🗣️OpenAI Chatbot"):
        gr.Markdown("Interact with an OpenAI chatbot that can respond to your messages. Type your message in the input box and click the 'SEND' button to send it to the chatbot.")
        chatbot = gr.Chatbot()
        message = gr.Textbox(placeholder=prompt)
        state = gr.State()
        submit = gr.Button("Send")
        submit.click(chatgpt_clone, inputs=[message, state], outputs=[chatbot, state])
    with gr.Tab("🤗Sentiment Analysis"):
        gr.Markdown("Perform sentiment analysis on a Chinese sentence. Enter a sentence in Chinese in the input box and click the 'proceed' button to get the sentiment analysis result.")
        inputs = gr.Textbox(placeholder="Type a Chinese sentence here, either positive or negative in sentiment.")
        outputs = gr.Textbox(label="Sentiment Analysis")
        proceed_button = gr.Button("Proceed")           
        proceed_button.click(fn=sentiment_analysis, inputs=inputs, outputs=outputs)
    with gr.Tab("🀄Translation to Chinese"):
        gr.Markdown("Translate an English sentence to Chinese. Enter an English sentence in the input box and click the 'Translate' button to get the translation result in Chinese.")
        inputs = gr.Textbox(placeholder="Enter a short English sentence to translate to Chinese here.")
        outputs = gr.Textbox(label="Translation Result")
        proceed_button = gr.Button("Translate")
        proceed_button.click(fn=translate_to_chinese, inputs=inputs, outputs=outputs)
    with gr.Tab("🔤Translation to English"):
        gr.Markdown("Translate a Chinese sentence to English. Enter a Chinese sentence in the input box and click the 'Translate' button to get the translation result in English.")
        inputs = gr.Textbox(placeholder="Enter a short Chinese sentence to translate to English here.")
        outputs = gr.Textbox(label="Translation Result")
        proceed_button = gr.Button("Translate")
        proceed_button.click(fn=translate_to_english, inputs=inputs, outputs=outputs)



demo.launch(inline=False)