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import gradio as gr
import json
import requests
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = 'Pyg'
tokenizer = AutoTokenizer.from_pretrained("TheBloke/Pygmalion-7B-SuperHOT-8K-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/Pygmalion-7B-SuperHOT-8K-GPTQ")
pipe = pipeline("text-generation", model="TheBloke/Pygmalion-7B-SuperHOT-8K-GPTQ")
def generate_text(input_text):
input_ids = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(input_ids, max_length=150, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return text
iface = gr.Interface(fn=generate_text,
inputs=gr.inputs.Textbox(lines=5, placeholder='Enter text here...'),
outputs=gr.outputs.Textbox())
iface.launch() |