Update app.py

app.py

@@ -1,39 +1,19 @@
 import streamlit as st
 import time
-import tokenizers
 from transformers import pipeline
 import torch
-#from transformers import AutoModelForCausalLM, AutoTokenizer
 
-#@st.cache(allow_output_mutation=True)
-#def define_model():
-#    model = AutoModelForCausalLM.from_pretrained("facebook/opt-1.3b", torch_dtype=torch.float16).cuda()
-#    tokenizer = AutoTokenizer.from_pretrained("facebook/opt-1.3b", use_fast=False)
-#    return model, tokenizer
 st.markdown('## OPT-1.3 Billion parameter (Meta)')
 
 with st.spinner('Loading Model... (This may take a while)'):
     generator = pipeline('text-generation', model="facebook/opt-1.3b", skip_special_tokens=True)    
     st.success('Model loaded correctly!')
 
-#@st.cache(allow_output_mutation=True)
-#def opt_model(prompt, model, tokenizer, num_sequences = 1, max_length = 50):  
-#    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
-#    generated_ids = model.generate(input_ids, num_return_sequences=num_sequences, max_length=max_length)
-#    answer = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
-#    return answer
-
-
-#model, tokenizer = define_model()
-
 prompt= st.text_area('Your prompt here',
  '''Hello, I'm am conscious and''') 
  
 answer = generator(prompt, max_length=100, no_repeat_ngram_size=3, early_stopping=True, num_beams=5)
  
-#answer = opt_model(prompt, model, tokenizer,)
-#lst = ['ciao come stai sjfsbd dfhsdf  fuahfuf  feuhfu wefwu ']
-#answer = define_model(prompt)
 lst = answer[0]['generated_text']
 
 t = st.empty()