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import gradio as gr |
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import os |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline |
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CHECKPOINT_PATH= 'scratch_2-nodes_tokenizer_latbert-original_packing_fcocchi/model.safetensors' |
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CHECKPOINT_PATH= 'itserr/latin_llm_alpha' |
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print(f"Loading model from: {CHECKPOINT_PATH}") |
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tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_PATH, token=st.secrets["HF_TOKEN"]) |
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model = AutoModelForCausalLM.from_pretrained(CHECKPOINT_PATH, token=st.secrets["HF_TOKEN"]) |
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description=""" |
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This is a Latin Language Model (LLM) based on GPT-2 and it was trained on a large corpus of Latin texts and can generate text in Latin. |
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Please enter a prompt in Latin to generate text. |
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""" |
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title= "(L<sup>3</sup>) - Latin Large Language Model" |
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article= "hello world ..." |
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examples= ['Accidere ex una scintilla', 'Audacter calumniare,', 'Consolatium misero comites'] |
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logo_image= 'ITSERR_row_logo.png' |
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def generate_text(prompt): |
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if torch.cuda.is_available(): device = torch.device("cuda") |
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else: |
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device = torch.device("cpu") |
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print("No GPU available") |
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print("***** Generate *****") |
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text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device) |
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generated_text = text_generator(prompt, max_length=50, do_sample=True, temperature=1.0, repetition_penalty=2.0, truncation=True) |
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return generated_text[0]['generated_text'] |
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custom_css = """ |
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#logo { |
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display: block; |
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margin-left: auto; |
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margin-right: auto; |
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width: 512px; |
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height: 256px; |
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} |
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""" |
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with gr.Blocks(css=custom_css) as demo: |
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gr.Image(logo_image, elem_id="logo") |
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gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>") |
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gr.Markdown(description) |
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with gr.Row(): |
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with gr.Column(): |
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input_text = gr.Textbox(lines=5, placeholder="Enter latin text here...", label="Input Text") |
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with gr.Column(): |
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output_text = gr.Textbox(lines=5, placeholder="Output text will appear here...", label="Output Text") |
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clean_button = gr.Button("Generate Text") |
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clean_button.click(fn=generate_text, inputs=input_text, outputs=output_text) |
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gr.Examples(examples=examples, inputs=input_text) |
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gr.Markdown(article) |
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demo.launch(share=True) |
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