Delete app.py

app.py

@@ -1,118 +0,0 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import nltk
-import math
-import torch
-
-model_name = "afnanmmir/t5-base-abstract-to-plain-language-1"
-# model_name = "afnanmmir/t5-base-axriv-to-abstract-3"
-max_input_length = 1024
-max_output_length = 256
-
-st.header("Generate summaries")
-
-st_model_load = st.text('Loading summary generator model...')
-
-# @st.cache(allow_output_mutation=True)
-@st.cache_data
-def load_model():
-    print("Loading model...")
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-    nltk.download('punkt')
-    print("Model loaded!")
-    return tokenizer, model
-
-tokenizer, model = load_model()
-st.success('Model loaded!')
-st_model_load.text("")
-
-# with st.sidebar:
-#     st.header("Model parameters")
-#     if 'num_titles' not in st.session_state:
-#         st.session_state.num_titles = 5
-#     def on_change_num_titles():
-#         st.session_state.num_titles = num_titles
-#     num_titles = st.slider("Number of titles to generate", min_value=1, max_value=10, value=1, step=1, on_change=on_change_num_titles)
-#     if 'temperature' not in st.session_state:
-#         st.session_state.temperature = 0.7
-#     def on_change_temperatures():
-#         st.session_state.temperature = temperature
-#     temperature = st.slider("Temperature", min_value=0.1, max_value=1.5, value=0.6, step=0.05, on_change=on_change_temperatures)
-#     st.markdown("_High temperature means that results are more random_")
-
-if 'text' not in st.session_state:
-    st.session_state.text = ""
-st_text_area = st.text_area('Text to generate the summary for', value=st.session_state.text, height=500)
-
-def generate_summary():
-    st.session_state.text = st_text_area
-
-    # tokenize text
-    inputs = ["summarize: " + st_text_area]
-    # print(inputs)
-    inputs = tokenizer(inputs, return_tensors="pt", max_length=max_input_length, truncation=True)
-    # print("Tokenized inputs: ")
-    # print(inputs)
-
-    # inputs = tokenizer(inputs, return_tensors="pt")
-
-    # # compute span boundaries
-    # num_tokens = len(inputs["input_ids"][0])
-    # print(f"Input has {num_tokens} tokens")
-    # max_input_length = 500
-    # num_spans = math.ceil(num_tokens / max_input_length)
-    # print(f"Input has {num_spans} spans")
-    # overlap = math.ceil((num_spans * max_input_length - num_tokens) / max(num_spans - 1, 1))
-    # spans_boundaries = []
-    # start = 0
-    # for i in range(num_spans):
-    #     spans_boundaries.append([start + max_input_length * i, start + max_input_length * (i + 1)])
-    #     start -= overlap
-    # print(f"Span boundaries are {spans_boundaries}")
-    # spans_boundaries_selected = []
-    # j = 0
-    # for _ in range(num_titles):
-    #     spans_boundaries_selected.append(spans_boundaries[j])
-    #     j += 1
-    #     if j == len(spans_boundaries):
-    #         j = 0
-    # print(f"Selected span boundaries are {spans_boundaries_selected}")
-
-    # # transform input with spans
-    # tensor_ids = [inputs["input_ids"][0][boundary[0]:boundary[1]] for boundary in spans_boundaries_selected]
-    # tensor_masks = [inputs["attention_mask"][0][boundary[0]:boundary[1]] for boundary in spans_boundaries_selected]
-
-    # inputs = {
-    #     "input_ids": torch.stack(tensor_ids),
-    #     "attention_mask": torch.stack(tensor_masks)
-    # }
-
-    # compute predictions
-    # outputs = model.generate(**inputs, do_sample=True, temperature=temperature, max_length=max_output_length)
-    
-    outputs = model.generate(**inputs, do_sample=True, max_length=max_output_length, early_stopping=True, num_beams=8, length_penalty=2.0, no_repeat_ngram_size=2, min_length=64)
-    # print("outputs", outputs)
-    decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
-    # print("Decoded_outputs", decoded_outputs)
-    predicted_summaries = nltk.sent_tokenize(decoded_outputs.strip())
-    print("Predicted summaries", predicted_summaries)
-
-    # decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
-    # predicted_summaries = [nltk.sent_tokenize(decoded_output.strip())[0] for decoded_output in decoded_outputs]
-
-    st.session_state.summaries = predicted_summaries
-
-# generate title button
-st_generate_button = st.button('Generate summary', on_click=generate_summary)
-
-# title generation labels
-if 'summaries' not in st.session_state:
-    st.session_state.summaries = []
-
-if len(st.session_state.summaries) > 0:
-    print("In summaries if")
-    with st.container():
-        st.subheader("Generated summaries")
-        for summary in st.session_state.summaries:
-            st.markdown("__" + summary + "__")