Create app.py

app.py

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+# 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")
+    retutn 'hello world'
+    
+    #print("***** Generate *****")
+    #text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device)
+    #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.Markdown(f"<h1 style='text-align: center;'>{title}</h1>")
+    
+    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)
+
+demo.launch(share=True)