app.py

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


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

def openai_chatbot(prompt):
    # Set up the OpenAI API client
    openai.api_key = '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'

    # Set up the model and prompt
    model_engine = "text-davinci-003"

    # Generate a response
    completion = openai.Completion.create(
        engine=model_engine,
        prompt=prompt,
        max_tokens=1024,
        n=1,
        stop=None,
        temperature=0.5,
    )

    response = completion.choices[0].text

    return f'🤖 {response}'

with gr.Blocks() as demo:
    gr.Markdown("Choose the Chinese NLP model you want to use.")
    with gr.Tab("Sentiment Analysis"):
        text_button = gr.Button("proceed")          
        text_button.click(fn=sentiment_analysis,inputs=gr.Textbox(placeholder="Enter a positive or negative sentence here..."),
                 outputs=gr.Textbox(label="Sentiment Analysis"))
    with gr.Tab("General Chatbot"):
        text_button = gr.Button("proceed")          
        text_button.click(fn=openai_chatbot,inputs=gr.Textbox(placeholder="Enter any topic you would like to discuss in Chinese"),
                 outputs=gr.Textbox(label="Chatbot Response"))


demo.launch(inline=False)