app.py

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import pipeline
import torch
import gradio as gr

# chatgpt-gpt4-prompts-bart-large-cnn-samsum
tokenizer = AutoTokenizer.from_pretrained(
    "Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum")
model = AutoModelForSeq2SeqLM.from_pretrained(
    "Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum", from_tf=True)

# zephyr
pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha",
                torch_dtype=torch.bfloat16, device_map="auto")


def useZephyr(prompt):
    messages = [
        {
            "role": "system",
            "content": "you are a chatbot who always responds politely and in the shortest possible way",
        },
        {"role": "user", "content": prompt},
    ]
    # https://huggingface.co/docs/transformers/main/en/chat_templating
    prompt = pipe.tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True)

    outputs = pipe(prompt)
    return outputs[0]["generated_text"]


def generatePrompt(prompt):
    batch = tokenizer(prompt, return_tensors="pt")
    generated_ids = model.generate(batch["input_ids"])
    output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
    prompt = output[0]

    result = useZephyr(prompt)
    
    return result 
#

# Interface


input_prompt = gr.Textbox(label="Prompt", value="photographer")
output_component = gr.Textbox(label="Output")
examples = [["photographer"], ["developer"], ["teacher"], [
    "human resources staff"], ["recipe for ham croquettes"]]
description = ""

PerfectGPT = gr.Interface(generatePrompt, inputs=input_prompt, outputs=output_component,
                          examples=examples, title="🗿 PerfectGPT v1 🗿", description=description)

PerfectGPT.launch()