Delete app_old.py

app_old.py

@@ -1,67 +0,0 @@
-# Run the script and open the link in the browser.
-
-import os
-import gradio as gr
-import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-
-# scratch with latbert tokenizer
-CHECKPOINT_PATH= 'scratch_2-nodes_tokenizer_latbert-original_packing_fcocchi/model.safetensors'
-CHECKPOINT_PATH= 'itserr/latin_llm_alpha'
-
-print(f"Loading model from: {CHECKPOINT_PATH}")
-tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_PATH, token=st.secrets["HF_TOKEN"])
-model = AutoModelForCausalLM.from_pretrained(CHECKPOINT_PATH, token=st.secrets["HF_TOKEN"])
-
-description="""
-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. 
-Please enter a prompt in Latin to generate text.
-"""
-title= "(L<sup>3</sup>) - Latin Large Language Model"
-article= "hello world ..."
-examples= ['Accidere ex una scintilla', 'Audacter calumniare,', 'Consolatium misero comites']
-logo_image= 'ITSERR_row_logo.png'
-
-def generate_text(prompt):
- if torch.cuda.is_available(): device = torch.device("cuda") 
- else: 
- device = torch.device("cpu")
- print("No GPU available")
- 
- print("***** Generate *****")
- text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device)
- #generated_text = text_generator(prompt, max_length=100)
- generated_text = text_generator(prompt, max_length=50, do_sample=True, temperature=1.0, repetition_penalty=2.0, truncation=True)
- return generated_text[0]['generated_text']
-
-custom_css = """
-#logo {
- display: block;
- margin-left: auto;
- margin-right: auto;
- width: 512px;
- height: 256px;
-}
-"""
-
-with gr.Blocks(css=custom_css) as demo:
- gr.Image(logo_image, elem_id="logo")
- gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>")
- gr.Markdown(description)
- 
- with gr.Row():
- with gr.Column():
- input_text = gr.Textbox(lines=5, placeholder="Enter latin text here...", label="Input Text")
- with gr.Column():
- output_text = gr.Textbox(lines=5, placeholder="Output text will appear here...", label="Output Text")
- 
- clean_button = gr.Button("Generate Text")
- clean_button.click(fn=generate_text, inputs=input_text, outputs=output_text)
- 
- gr.Examples(examples=examples, inputs=input_text)
- gr.Markdown(article)
-
-demo.launch(share=True)
-# iface = gr.Interface(fn=generate_text, inputs="text", outputs="text", 
-# description=description, title=title, examples=examples, article=article)