Update app.py

app.py

@@ -1,37 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import streamlit as st
-import torch
-
-@st.cache_resource
-def load_model(cp_path):
-    model = AutoModelForSeq2SeqLM.from_pretrained(cp_path)
-    return model
-    
-@st.cache_resource
-def load_tokenizer(path):
-    tokenizer = AutoTokenizer.from_pretrained(path)
-
-cp_aug = 'minnehwg/finetune-newwiki-summarization-ver-augmented2'
-cp_org = 'minnehwg/finetune-newwiki-summarization-ver2'
-
-model_org = load_model(cp_org)
-model_aug  = AutoModelForSeq2SeqLM.from_pretrained(cp_aug)
-tokenizer = load_tokenizer("VietAI/vit5-base")
-
-def summarize(text, model, tokenizer, num_beams=4, device='cpu'):
-    model.eval()
-    model.to(device)
-    inputs = tokenizer.encode(text, return_tensors="pt", max_length=1024, truncation=True, padding = True).to(device)
-    with torch.no_grad():
-        summary_ids = model.generate(inputs, max_length=256, num_beams=num_beams)
-    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-    return summary
-
-if text:
-    re1 = summarize(model_org, tokenizer, text)
-    re2 = summarize(model_aug, tokenizer, text)
-    out = {
-        'Result from model with original data': re1,
-        'Result from model with augmented data': re2
-    }
-    st.json(out)