Create app.py

app.py

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+# from transformers import AutoModelForCausalLM, AutoTokenizer
+
+!pip install --no-cache-dir transformers sentencepiece
+
+import time
+import datetime
+
+import streamlit as st
+
+question = "Name the planets in the solar system? A: "
+question = "Quais são os planetas do sistema solar?" 
+question = "Qual é o maior planeta do sistema solar?" 
+
+before = datetime.datetime.now()
+
+from transformers import AutoTokenizer, XGLMModel
+import torch
+
+prompt = "Question: Qual é o maior planeta do sistema solar ?"
+tokenizer = AutoTokenizer.from_pretrained("facebook/xglm-564M",  use_fast=False)
+model = XGLMModel.from_pretrained("facebook/xglm-564M")
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model(**inputs, labels=inputs["input_ids"])
+
+last_hidden_states = outputs.last_hidden_state
+
+output = last_hidden_states
+
+output = tokenizer.batch_decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+with st.container():
+    st.write('\n\n')
+    st.write('LLM-LANAChat')
+    st.write('\n\n' + output)
+
+print('saida gerada.')
+print('\n\n')
+
+after = datetime.datetime.now()
+current_time = (after - before) # .strftime("%H:%M:%S")
+print("\nTime Elapsed: ", current_time)
+st.write("\nTime Elapsed: ", current_time)