Create app.py

app.py

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+from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+import torch
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-xl", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-xl", pad_token_id=tokenizer.eos_token_id)
+model.to(device)
+
+
+def predict(input, history=None):
+    if history is None:
+        history = []
+    response, history = model.chat(tokenizer, input, history)
+    return history, history
+
+
+with gr.Blocks() as demo:
+    gr.Markdown('''## ArikGPT - Trained on a dataset of chats, jokes and news in the Hebrew language''')
+    state = gr.State([])
+    chatbot = gr.Chatbot([], elem_id="chatbot").style(height=400)
+    with gr.Row():
+        with gr.Column(scale=4):
+            txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
+        with gr.Column(scale=1):
+            button = gr.Button("Generate")
+    txt.submit(predict, [txt, state], [chatbot, state])
+    button.click(predict, [txt, state], [chatbot, state])
+    
+demo.queue().launch()
+
+# Based on  EleutherAI's gpt-neo 1.37B params