app.py

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

tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-base", torch_dtype=torch.bfloat16)

print()


# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")

def gentext(user_input="The world is"):
    inputs = tokenizer(user_input, return_tensors="pt")
    sample = model.generate(**inputs, max_length=128)
    
    return {"output": tokenizer.decode(sample[0])}


gr.Interface(
    gentext,
    inputs=gr.inputs.Text(label="Some prompt", type="input"),
    outputs=gr.outputs.Label(num_top_classes=2),
    title="Some prompt",
).launch()