Create app.py

app.py

@@ -0,0 +1,36 @@
+import gradio as gr
+import re
+import time
+import streamlit as st
+from transformers import pipeline, Conversation, AutoTokenizer
+#"meta-llama/Llama-2-13b-chat-hf"
+my_config = {'model_name': "BramVanroy/Llama-2-13b-chat-dutch", 'do_sample': True, 'temperature': 0.1, 'repetition_penalty': 1.1, 'max_new_tokens': 500}
+
+print(f"Loading the model: {my_config['model_name']}....")
+time_load_model_start = time.time()
+
+print(time_load_model_start)
+
+# Load the model and tokenizer outside of the functions
+llm = pipeline("text-generation",
+                model=my_config['model_name'],
+                #tokenizer=AutoTokenizer.from_pretrained(my_config['model_name']),
+                #do_sample=my_config['do_sample'],
+                ##temperature=my_config['temperature'],
+                #repetition_penalty=my_config['repetition_penalty'],
+                #max_new_tokens=my_config['max_new_tokens']
+              )
+time_load_model_end = time.time()
+elapsed_time = time_load_model_end - time_load_model_start
+print(f"Elapsed time to load the model: {elapsed_time:.2f} sec")
+
+
+def get_answer(llm):
+
+    return "tekst output"
+
+
+#gr.ChatInterface(get_llama_response).launch()
+demo = gr.Interface(fn=get_answer, inputs="text", outputs="text")
+
+demo.launch(share=True)