Delete app.py

app.py

@@ -1,157 +0,0 @@
-import streamlit as st
-import time
-
-# model part
-
-import json
-import torch
-from torch import nn
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-with open('categories_with_names.json', 'r') as f:
-    cat_with_names = json.load(f)
-with open('categories_from_model.json', 'r') as f:
-    categories_from_model = json.load(f)
-
-@st.cache_resource
-def load_models_and_tokenizer():
-    tokenizer = AutoTokenizer.from_pretrained("oracat/bert-paper-classifier-arxiv")
-
-    model_titles = AutoModelForSequenceClassification.from_pretrained(
-        "powerful_model_titles/checkpoint-13472", num_labels=len(categories_from_model), problem_type="multi_label_classification"
-    )
-    model_titles.eval()
-    model_abstracts = AutoModelForSequenceClassification.from_pretrained(
-        "powerful_model_abstracts/checkpoint-13472", num_labels=len(categories_from_model), problem_type="multi_label_classification"
-    )
-    model_abstracts.eval()
-    
-    return model_titles, model_abstracts, tokenizer
-
-model_titles, model_abstracts, tokenizer = load_models_and_tokenizer()
-
-def categorize_text(title: str | None = None, abstract: str | None = None, progress_bar = None):
-    if title is None and abstract is None:
-        raise ValueError('title is None and abstract is None')
-
-    models_to_run = 2 if (title is not None and abstract is not None) else 1
-        
-    proba_title = None
-    if title is not None:
-        progresses = (10, 30) if models_to_run == 2 else (20, 60)
-        my_bar.progress(progresses[0], text='computing titles')
-        input_tok = tokenizer(title, return_tensors='pt')
-        with torch.no_grad():
-            logits = model_titles(**input_tok)['logits']
-            proba_title = torch.sigmoid(logits)[0]
-        my_bar.progress(progresses[1], text='computed titles')
-            
-    proba_abstract = None
-    if abstract is not None:
-        progresses = (40, 70) if models_to_run == 2 else (20, 60)
-        my_bar.progress(progresses[0], text='computing abstracts')
-        input_tok = tokenizer(abstract, return_tensors='pt')
-        with torch.no_grad():
-            logits = model_abstracts(**input_tok)['logits']
-            proba_abstract = torch.sigmoid(logits)[0]
-        my_bar.progress(progresses[0], text='computed abstracts')
-            
-    if title is None:
-        proba = proba_abstract
-    elif abstract is None:
-        proba = proba_title
-    else:
-        proba = proba_title * 0.1 + proba_abstract * 0.9
-
-    progresses = (80, 90) if models_to_run == 2 else (70, 90)
-
-    my_bar.progress(progresses[0], text='computed proba')
-    
-    sorted_proba, indices = torch.sort(proba, descending=True)
-    my_bar.progress(progresses[1], text='sorted proba')
-    to_take = 1
-    while sorted_proba[:to_take].sum() < 0.95 and to_take < len(categories_from_model):
-        to_take += 1
-    output = [(cat_with_names[categories_from_model[index]], proba[index].item())
-            for index in indices[:to_take]]
-    my_bar.progress(100, text='generated output')
-    return output
-
-# front part
-
-st.markdown("<h1 style='text-align: center;'>Classify your paper!</h1>", unsafe_allow_html=True)
-
-
-if "title" not in st.session_state:
-    st.session_state.title = ""
-if "abstract" not in st.session_state:
-    st.session_state.abstract = ""
-if "title_input_key" not in st.session_state:
-    st.session_state.title_input_key = ""
-if "abstract_input_key" not in st.session_state:
-    st.session_state.abstract_input_key = ""
-if "model_type" not in st.session_state:
-    st.session_state.model_type = []
-
-def input_error():
-    if not st.session_state.model_type:
-        return 'you have to select title or abstract'
-    if 'Title' in model_type and not st.session_state.title:
-        return 'Title is empty'
-    if 'Abstract' in model_type and not st.session_state.abstract:
-        return 'Abstract is empty'
-    return ''
-
-
-def clear_input():
-    st.session_state.title = title.title()
-    st.session_state.abstract = abstract.title()
-    if not input_error():
-        if "Title" in st.session_state.model_type:
-           st.session_state.title_input_key = ""
-        if "Abstract" in st.session_state.model_type:
-           st.session_state.abstract_input_key = ""
-
-
-title = st.text_input(r"$\textsf{\Large Title}$", key="title_input_key")
-
-abstract = st.text_input(r"$\textsf{\Large Abstract}$", key="abstract_input_key")
-
-
-model_type = st.multiselect(
-    r"$\textsf{\large Classify by:}$",
-    ['Title', 'Abstract'],
-)
-
-st.session_state.model_type = model_type
-
-if(st.button('Submit', on_click=clear_input)):
-    if input_error():
-        st.error(input_error())
-    else:
-        send_time = time.localtime(time.time())
-        #st.success(f"Submitted {(' and '.join(st.session_state.model_type)).lower()} on {time.strftime('%d.%m %H:%M:%S', send_time)}")
-        model_input = dict()
-        if 'Title' in st.session_state.model_type:
-            model_input['title'] = st.session_state.title
-        if 'Abstract' in st.session_state.model_type:
-            model_input['abstract'] = st.session_state.abstract
-        #st.success(f'{model_input=}')
-        my_bar = st.progress(0, text='starting model')
-        model_result = categorize_text(**model_input, progress_bar=my_bar)
-        st.markdown("<h1 style='text-align: center;'>Classification completed!</h1>", unsafe_allow_html=True)
-        small_categories = []
-        cat, proba = model_result[0]
-        st.write(r"$\textsf{\Large " + f'{cat}: {round(100*proba)}' + r"\%}$")
-        for cat, proba in model_result[1:]:
-            if proba < 0.1:
-                small_categories.append(f'{cat}: {round(100*proba, 1)}' + r"\%")
-            else:
-                st.write(r"$\textsf{\large " + f'{cat}: {round(100*proba)}' + r"\%}$")
-        if small_categories:
-            st.write(', '.join(small_categories))
-
-
-
-
-