app.py

import openai
import gradio

openai.api_key = "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"

#messages = [{"role": "system", "content": "You are a financial expert that specializes in providing actionable support to your customers in the field of finance.You always reply in English, German and Hindi. You only talk about topics related to finance. The recommendation should be categorized in 3 categories. 1st is Young Adults under the age of 25, second is adults between 25 and 60 years and the third category is Senior citizen above 60 years."
#}]
#messages = [{"role": "system", "content": "You generate output in a sequential and organized manner."
#}]

messages = [{"role": "system", "content": "Generate the output as required by the user in hindi language. If not specified, ask the user to specify a word count; otherwise, generate an output in detail. Also give the word count and character count of the generated text."
}]
def CustomChatGPT(query):
    messages.append({"role": "user", "content": query})
    response = openai.ChatCompletion.create(
        model = "gpt-3.5-turbo",
        messages = messages
    )
    ChatGPT_reply = response["choices"][0]["message"]["content"]
    messages.append({"role": "assistant", "content": ChatGPT_reply})
    return ChatGPT_reply

demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text", title = "Personal Chat bot powered by GPT 3.5 Turbo")

demo.launch()