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import gradio as gr
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
# Initialisierung des Modells und des Tokenizers
tokenizer = GPT2Tokenizer.from_pretrained("Loewolf/GPT_1")
model = GPT2LMHeadModel.from_pretrained("Loewolf/GPT_1")
def generate_text(prompt):
input_ids = tokenizer.encode(prompt, return_tensors="pt")
attention_mask = torch.ones(input_ids.shape, dtype=torch.bool)
max_length = model.config.n_positions if len(input_ids[0]) > model.config.n_positions else len(input_ids[0]) + 50
beam_output = model.generate(
input_ids,
attention_mask=attention_mask,
max_length=max_length,
min_length=4,
num_beams=5,
no_repeat_ngram_size=2,
early_stopping=True,
temperature=0.9,
top_p=0.90,
top_k=50,
length_penalty=2.0,
do_sample=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id
)
text = tokenizer.decode(beam_output[0], skip_special_tokens=True)
return text
DESCRIPTION = """\
#Löwolf GPT1 Chat
"""
css = """
body { font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; }
.gradio_app { display: flex; flex-direction: column-reverse; max-width: 800px; margin: auto; }
.gradio_interface { box-shadow: 0 0 20px rgba(0,0,0,0.1); }
.gradio_text_input { border-radius: 20px; }
.gradio_text_output { height: calc(100vh - 150px); border-radius: 20px; overflow-y: auto; }
button { border-radius: 20px; }
#input-box { order: 2; }
#output-box { order: 1; }
#input { height: 50px; }
#output { height: calc(100vh - 150px); }
"""
# Update the Blocks structure to reflect the desired layout
with gr.Blocks(css=css) as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
with gr.Row().style(equal_height=False):
output_box = gr.Textbox(interactive=False, lines=25, placeholder="Responses will appear here...", elem_id="output-box")
input_box = gr.Textbox(lines=1, placeholder="Type a message...", elem_id="input-box")
generate_button = gr.Button("Submit")
generate_button.click(
fn=generate,
inputs=[input_box],
outputs=output_box
)
if __name__ == "__main__":
demo.queue(max_size=20).launch()
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