Update app.py

app.py

@@ -1,322 +1,322 @@
-import itertools
-import json
-import re
-from functools import partial
-from pathlib import Path
-
-import pandas as pd
-import requests
-import streamlit as st
-import webvtt
-from transformers import AutoTokenizer
-
-from generate_text_api import TextGenerator
-from model_inferences.utils.chunking import Truncater
-from model_inferences.utils.files import get_captions_from_vtt, get_transcript
-
-USE_PARAGRAPHING_MODEL = True
-
-def get_sublist_by_flattened_index(A, i):
-    current_index = 0
-    for sublist in A:
-        sublist_length = len(sublist)
-        if current_index <= i < current_index + sublist_length:
-            return sublist, A.index(sublist)
-        current_index += sublist_length
-    return None, None
-
-import requests
-
-
-def get_talk_metadata(video_id):
-    url = "https://www.ted.com/graphql"
-
-    headers = {
-        "Content-Type": "application/json",
-        "Accept": "application/json",
-        "x-operation-name": "Transcript",  # Replace with the actual operation name
-    }
-
-    data = {
-        "query": """
-        query GetTalk($videoId: ID!) {
-            video(id: $videoId) {
-                title,
-                presenterDisplayName,
-                nativeDownloads {medium}
-            }
-        }
-        """,
-        "variables": {
-            "videoId": video_id,  # Corrected key to "videoId"
-        },
-    }
-
-    response = requests.post(url, json=data, headers=headers)
-
-    if response.status_code == 200:
-        result = response.json()
-        return result
-    else:
-        print(f"Error: {response.status_code}, {response.text}")
-
-class OfflineTextSegmenterClient:
-    def __init__(self, host_url):
-        self.host_url = host_url.rstrip("/") + "/segment"
-
-    def segment(self, text, captions=None, generate_titles=False, threshold=0.4):
-        payload = {
-            'text': text,
-            'captions': captions,
-            'generate_titles': generate_titles,
-            "prefix_titles": True,
-            "threshold": threshold,
-        }
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-
-        response = requests.post(self.host_url, data=json.dumps(payload), headers=headers).json()
-        #segments =  response["annotated_segments"] if "annotated_segments" in response else response["segments"]
-        return {'segments':response["segments"], 'titles': response["titles"], 'sentences': response["sentences"]}
-
-class Toc:
-
-    def __init__(self):
-        self._items = []
-        self._placeholder = None
-    
-    def title(self, text):
-        self._markdown(text, "h1")
-
-    def header(self, text):
-        self._markdown(text, "h2", " " * 2)
-
-    def subheader(self, text):
-        self._markdown(text, "h3", " " * 4)
-
-    def placeholder(self, sidebar=False):
-        self._placeholder = st.sidebar.empty() if sidebar else st.empty()
-
-    def generate(self):
-        if self._placeholder:
-            self._placeholder.markdown("\n".join(self._items), unsafe_allow_html=True)
-    
-    def _markdown(self, text, level, space=""):
-        key = re.sub(r'[^\w-]', '', text.replace(" ", "-").replace("'", "-").lower())
-        st.markdown(f"<{level} id='{key}'>{text}</{level}>", unsafe_allow_html=True)
-        self._items.append(f"{space}* <a href='#{key}'>{text}</a>")
-
-# custom_css = "<style type='text/css'>" + Path('style.css').read_text() + "</style>"
-# st.write(custom_css, unsafe_allow_html=True)
-
-def concat_prompt(prompt_text, text, model_name):
-    if 'flan' in model_name:
-        input_ = prompt_text + "\n\n" + text
-    elif 'galactica' in model_name:
-        input_ = text + "\n\n" + prompt_text
-    return input_
-
-endpoint = "http://hiaisc.isl.iar.kit.edu/summarize"
-ENDPOINTS = {"http://hiaisc.isl.iar.kit.edu/summarize": "meta-llama/Llama-2-13b-chat-hf",}
-
-client = OfflineTextSegmenterClient("http://hiaisc.isl.iar.kit.edu/chapter")
-if USE_PARAGRAPHING_MODEL:
-    paragrapher = OfflineTextSegmenterClient("http://hiaisc.isl.iar.kit.edu/paragraph")
-summarizer = TextGenerator(endpoint)
-
-tokenizer = AutoTokenizer.from_pretrained(ENDPOINTS[endpoint], use_fast=False)
-
-# TLDR PROMPT
-
-SYSTEM_PROMPT = "You are an assistant who replies with a summary to every message."
-
-TLDR_PROMPT_TEMPLATE = """<s>[INST] <<SYS>>
-{system_prompt}
-<</SYS>>
-
-{user_message} [/INST] Sure! Here is a summary of the research presentation in a single, short sentence:"""
-
-TLDR_USER_PROMPT = "Summarize the following research presentation in a single, short sentence:\n\n{input}"
-
-TLDR_PROMPT = TLDR_PROMPT_TEMPLATE.format(system_prompt=SYSTEM_PROMPT, user_message=TLDR_USER_PROMPT)
-TLDR_PROMPT_LENGTH = tokenizer(TLDR_PROMPT, return_tensors="pt")["input_ids"].size(1)
-
-BP_PROMPT_TEMPLATE = """<s>[INST] <<SYS>>
-{system_prompt}
-<</SYS>>
-
-{user_message} [/INST] Sure! Here is a summary of the research presentation using three bullet points:\n\n\u2022"""
-
-BP_USER_PROMPT = "Summarize the following research presentation using three bullet points:\n\n{input}"
-
-BP_PROMPT = BP_PROMPT_TEMPLATE.format(system_prompt=SYSTEM_PROMPT, user_message=TLDR_USER_PROMPT)
-BP_PROMPT_LENGTH = tokenizer(BP_PROMPT, return_tensors="pt")["input_ids"].size(1)
-
-CONTEXT_LENGTH = 3072
-MAX_SUMMARY_LENGTH = 1024
-TLDR_MAX_INPUT_LENGTH = CONTEXT_LENGTH - MAX_SUMMARY_LENGTH - TLDR_PROMPT_LENGTH - 1
-BP_MAX_INPUT_LENGTH = CONTEXT_LENGTH - MAX_SUMMARY_LENGTH - BP_PROMPT_LENGTH - 1
-
-
-text_generator = TextGenerator(endpoint)
-temperature = 0.7
-
-import re
-
-
-def replace_newlines(text):
-    updated_text = re.sub(r'\n+', r'\n\n', text)
-    return updated_text
-
-def generate_summary(summarizer, generated_text_box, input_, prompt, max_input_length, prefix=""):
-    all_generated_text = prefix
-    truncater = Truncater(tokenizer, max_length=max_input_length)
-    input_ = truncater(input_)
-    input_ = prompt.format(input=input_)
-    for generated_text in summarizer.generate_text_stream(input_, max_new_tokens=MAX_SUMMARY_LENGTH, do_sample=True, temperature=temperature):
-        all_generated_text += replace_newlines(generated_text)
-        generated_text_box.info(all_generated_text)
-    print(all_generated_text)
-    return all_generated_text.strip()
-
-st.header("Demo: Intelligent Recap")
-
-if not hasattr(st, 'global_state'):
-    st.global_state = {'NIPS 2021 Talks': None, 'TED Talks': None}
-    # NIPS 2021 Talks
-    transcript_files = itertools.islice(Path("demo_data/nips-2021/").rglob("transcript_whisper_large-v2.vtt"), 15)
-    # get titles from metadata.json
-    transcripts_map = {}
-    for transcript_file in transcript_files:
-        base_path = transcript_file.parent
-        metadata = base_path / "metadata.json"
-        txt_file = base_path / "transcript_whisper_large-v2.txt"
-        with open(metadata) as f:
-            metadata = json.load(f)
-            title = metadata["title"]
-            transcript = get_transcript(txt_file)
-            captions = get_captions_from_vtt(transcript_file)
-            transcripts_map[title] = {"transcript": transcript, "captions": captions, "video": base_path / "video.mp4"}
-    st.global_state['NIPS 2021 Talks'] = transcripts_map
-
-    data = pd.read_json("demo_data/ted_talks.json")
-    video_ids = data.talk_id.tolist()
-    transcripts = data.text.apply(lambda x: " ".join(x)).tolist()
-    transcripts_map = {}
-    for video_id, transcript in zip(video_ids, transcripts):
-        metadata = get_talk_metadata(video_id)
-        title = metadata["data"]["video"]["title"]
-        presenter = metadata["data"]["video"]["presenterDisplayName"]
-        print(metadata["data"])
-        if metadata["data"]["video"]["nativeDownloads"] is None:
-            continue
-        video_url = metadata["data"]["video"]["nativeDownloads"]["medium"]
-        transcripts_map[title] = {"transcript": transcript, "video": video_url, "presenter": presenter}
-    st.global_state['TED Talks'] = transcripts_map
-
-    def get_lecture_id(path):
-        return int(path.parts[-2].split('-')[1])
-
-    transcript_files = Path("demo_data/lectures/").rglob("English.vtt")
-    sorted_path_list = sorted(transcript_files, key=get_lecture_id)
-
-    transcripts_map = {}
-    for transcript_file in sorted_path_list:
-        base_path = transcript_file.parent
-        lecture_id = base_path.parts[-1]
-        transcript = " ".join([c["text"].strip() for c in get_captions_from_vtt(transcript_file)]).replace("\n", " ")
-        video_path = Path(base_path, "video.mp4")
-        transcripts_map["Machine Translation: " + lecture_id] = {"transcript": transcript, "video": video_path}
-    st.global_state['KIT Lectures'] = transcripts_map
-
-type_of_document = st.selectbox('What kind of document do you want to test it on?', list(st.global_state.keys()))
-
-transcripts_map = st.global_state[type_of_document]
-
-selected_talk = st.selectbox("Choose a document...", list(transcripts_map.keys()))
-
-st.video(str(transcripts_map[selected_talk]['video']), format="video/mp4", start_time=0)
-
-input_text = st.text_area("Transcript", value=transcripts_map[selected_talk]['transcript'], height=300)
-
-toc = Toc()
-
-summarization_todos = []
-
-with st.expander("Adjust Thresholds"):
-    threshold = st.slider('Chapter Segmentation Threshold', 0.00, 1.00, value=0.4, step=0.05)
-    paragraphing_threshold = st.slider('Paragraphing Threshold', 0.00, 1.00, value=0.5, step=0.05)
-
-if st.button("Process Transcript"):
-    with st.sidebar:
-        st.header("Table of Contents")
-        toc.placeholder()
-
-    st.header(selected_talk, divider='rainbow')
-    # if 'presenter' in transcripts_map[selected_talk]:
-    #     st.markdown(f"### *by **{transcripts_map[selected_talk]['presenter']}***")
-
-    captions = transcripts_map[selected_talk]['captions'] if 'captions' in transcripts_map[selected_talk] else None
-    result = client.segment(input_text, captions, generate_titles=True, threshold=threshold)
-    if USE_PARAGRAPHING_MODEL:
-        presult = paragrapher.segment(input_text, captions, generate_titles=False, threshold=paragraphing_threshold)
-        paragraphs = presult['segments']
-    segments, titles, sentences = result['segments'], result['titles'], result['sentences']
-
-    if USE_PARAGRAPHING_MODEL:
-        prev_chapter_idx = 0
-        prev_paragraph_idx = 0
-        segment = []
-        for i, sentence in enumerate(sentences):
-            chapter, chapter_idx = get_sublist_by_flattened_index(segments, i)
-            paragraph, paragraph_idx = get_sublist_by_flattened_index(paragraphs, i)
-
-            if (chapter_idx != prev_chapter_idx and paragraph_idx == prev_paragraph_idx) or (paragraph_idx != prev_paragraph_idx and chapter_idx != prev_chapter_idx):
-                print("Chapter / Chapter & Paragraph")
-                segment_text = " ".join(segment)
-                toc.subheader(titles[prev_chapter_idx])
-                if len(segment_text) > 1200:
-                    generated_text_box = st.info("")
-                    summarization_todos.append(partial(generate_summary, summarizer, generated_text_box, segment_text, BP_PROMPT, BP_MAX_INPUT_LENGTH, prefix="\u2022"))
-                elif len(segment_text) > 450:
-                    generated_text_box = st.info("")
-                    summarization_todos.append(partial(generate_summary, summarizer, generated_text_box, segment_text, TLDR_PROMPT, TLDR_MAX_INPUT_LENGTH))
-                st.write(segment_text)
-                segment = []
-            elif paragraph_idx != prev_paragraph_idx and chapter_idx == prev_chapter_idx:
-                print("Paragraph")
-                segment.append("\n\n")
-            
-            segment.append(sentence)
-
-            prev_chapter_idx = chapter_idx
-            prev_paragraph_idx = paragraph_idx
-
-        segment_text = " ".join(segment)
-        toc.subheader(titles[prev_chapter_idx])
-        if len(segment_text) > 1200:
-            generated_text_box = st.info("")
-            summarization_todos.append(partial(generate_summary, summarizer, generated_text_box, segment_text, BP_PROMPT, BP_MAX_INPUT_LENGTH, prefix="\u2022"))
-        elif len(segment_text) > 450:
-            generated_text_box = st.info("")
-            summarization_todos.append(partial(generate_summary, summarizer, generated_text_box, segment_text, TLDR_PROMPT, TLDR_MAX_INPUT_LENGTH))
-        st.write(segment_text)
-
-
-    else:
-        segments = [" ".join([sentence for sentence in segment]) for segment in segments]
-        for title, segment in zip(titles, segments):
-            toc.subheader(title)
-            if len(segment) > 1200:
-                generated_text_box = st.info("")
-                summarization_todos.append(partial(generate_summary, summarizer, generated_text_box, segment, BP_PROMPT, BP_MAX_INPUT_LENGTH, prefix="\u2022"))
-            elif len(segment) > 450:
-                generated_text_box = st.info("")
-                summarization_todos.append(partial(generate_summary, summarizer, generated_text_box, segment, TLDR_PROMPT, TLDR_MAX_INPUT_LENGTH))
-            st.write(segment)
-    toc.generate()
-
-for summarization_todo in summarization_todos:
-    summarization_todo()
+import itertools
+import json
+import re
+from functools import partial
+from pathlib import Path
+
+import pandas as pd
+import requests
+import streamlit as st
+import webvtt
+from transformers import AutoTokenizer
+
+from generate_text_api import TextGenerator
+from model_inferences.utils.chunking import Truncater
+from model_inferences.utils.files import get_captions_from_vtt, get_transcript
+
+USE_PARAGRAPHING_MODEL = True
+
+def get_sublist_by_flattened_index(A, i):
+    current_index = 0
+    for sublist in A:
+        sublist_length = len(sublist)
+        if current_index <= i < current_index + sublist_length:
+            return sublist, A.index(sublist)
+        current_index += sublist_length
+    return None, None
+
+import requests
+
+
+def get_talk_metadata(video_id):
+    url = "https://www.ted.com/graphql"
+
+    headers = {
+        "Content-Type": "application/json",
+        "Accept": "application/json",
+        "x-operation-name": "Transcript",  # Replace with the actual operation name
+    }
+
+    data = {
+        "query": """
+        query GetTalk($videoId: ID!) {
+            video(id: $videoId) {
+                title,
+                presenterDisplayName,
+                nativeDownloads {medium}
+            }
+        }
+        """,
+        "variables": {
+            "videoId": video_id,  # Corrected key to "videoId"
+        },
+    }
+
+    response = requests.post(url, json=data, headers=headers)
+
+    if response.status_code == 200:
+        result = response.json()
+        return result
+    else:
+        print(f"Error: {response.status_code}, {response.text}")
+
+class OfflineTextSegmenterClient:
+    def __init__(self, host_url):
+        self.host_url = host_url.rstrip("/") + "/segment"
+
+    def segment(self, text, captions=None, generate_titles=False, threshold=0.4):
+        payload = {
+            'text': text,
+            'captions': captions,
+            'generate_titles': generate_titles,
+            "prefix_titles": True,
+            "threshold": threshold,
+        }
+
+        headers = {
+            'Content-Type': 'application/json'
+        }
+
+        response = requests.post(self.host_url, data=json.dumps(payload), headers=headers).json()
+        #segments =  response["annotated_segments"] if "annotated_segments" in response else response["segments"]
+        return {'segments':response["segments"], 'titles': response["titles"], 'sentences': response["sentences"]}
+
+class Toc:
+
+    def __init__(self):
+        self._items = []
+        self._placeholder = None
+    
+    def title(self, text):
+        self._markdown(text, "h1")
+
+    def header(self, text):
+        self._markdown(text, "h2", " " * 2)
+
+    def subheader(self, text):
+        self._markdown(text, "h3", " " * 4)
+
+    def placeholder(self, sidebar=False):
+        self._placeholder = st.sidebar.empty() if sidebar else st.empty()
+
+    def generate(self):
+        if self._placeholder:
+            self._placeholder.markdown("\n".join(self._items), unsafe_allow_html=True)
+    
+    def _markdown(self, text, level, space=""):
+        key = re.sub(r'[^\w-]', '', text.replace(" ", "-").replace("'", "-").lower())
+        st.markdown(f"<{level} id='{key}'>{text}</{level}>", unsafe_allow_html=True)
+        self._items.append(f"{space}* <a href='#{key}'>{text}</a>")
+
+# custom_css = "<style type='text/css'>" + Path('style.css').read_text() + "</style>"
+# st.write(custom_css, unsafe_allow_html=True)
+
+def concat_prompt(prompt_text, text, model_name):
+    if 'flan' in model_name:
+        input_ = prompt_text + "\n\n" + text
+    elif 'galactica' in model_name:
+        input_ = text + "\n\n" + prompt_text
+    return input_
+
+endpoint = "http://hiaisc.isl.iar.kit.edu/summarize"
+ENDPOINTS = {"http://hiaisc.isl.iar.kit.edu/summarize": "meta-llama/Llama-2-13b-chat-hf",}
+
+client = OfflineTextSegmenterClient("http://hiaisc.isl.iar.kit.edu/chapterize")
+if USE_PARAGRAPHING_MODEL:
+    paragrapher = OfflineTextSegmenterClient("http://hiaisc.isl.iar.kit.edu/paragraph")
+summarizer = TextGenerator(endpoint)
+
+tokenizer = AutoTokenizer.from_pretrained(ENDPOINTS[endpoint], use_fast=False)
+
+# TLDR PROMPT
+
+SYSTEM_PROMPT = "You are an assistant who replies with a summary to every message."
+
+TLDR_PROMPT_TEMPLATE = """<s>[INST] <<SYS>>
+{system_prompt}
+<</SYS>>
+
+{user_message} [/INST] Sure! Here is a summary of the research presentation in a single, short sentence:"""
+
+TLDR_USER_PROMPT = "Summarize the following research presentation in a single, short sentence:\n\n{input}"
+
+TLDR_PROMPT = TLDR_PROMPT_TEMPLATE.format(system_prompt=SYSTEM_PROMPT, user_message=TLDR_USER_PROMPT)
+TLDR_PROMPT_LENGTH = tokenizer(TLDR_PROMPT, return_tensors="pt")["input_ids"].size(1)
+
+BP_PROMPT_TEMPLATE = """<s>[INST] <<SYS>>
+{system_prompt}
+<</SYS>>
+
+{user_message} [/INST] Sure! Here is a summary of the research presentation using three bullet points:\n\n\u2022"""
+
+BP_USER_PROMPT = "Summarize the following research presentation using three bullet points:\n\n{input}"
+
+BP_PROMPT = BP_PROMPT_TEMPLATE.format(system_prompt=SYSTEM_PROMPT, user_message=TLDR_USER_PROMPT)
+BP_PROMPT_LENGTH = tokenizer(BP_PROMPT, return_tensors="pt")["input_ids"].size(1)
+
+CONTEXT_LENGTH = 3072
+MAX_SUMMARY_LENGTH = 1024
+TLDR_MAX_INPUT_LENGTH = CONTEXT_LENGTH - MAX_SUMMARY_LENGTH - TLDR_PROMPT_LENGTH - 1
+BP_MAX_INPUT_LENGTH = CONTEXT_LENGTH - MAX_SUMMARY_LENGTH - BP_PROMPT_LENGTH - 1
+
+
+text_generator = TextGenerator(endpoint)
+temperature = 0.7
+
+import re
+
+
+def replace_newlines(text):
+    updated_text = re.sub(r'\n+', r'\n\n', text)
+    return updated_text
+
+def generate_summary(summarizer, generated_text_box, input_, prompt, max_input_length, prefix=""):
+    all_generated_text = prefix
+    truncater = Truncater(tokenizer, max_length=max_input_length)
+    input_ = truncater(input_)
+    input_ = prompt.format(input=input_)
+    for generated_text in summarizer.generate_text_stream(input_, max_new_tokens=MAX_SUMMARY_LENGTH, do_sample=True, temperature=temperature):
+        all_generated_text += replace_newlines(generated_text)
+        generated_text_box.info(all_generated_text)
+    print(all_generated_text)
+    return all_generated_text.strip()
+
+st.header("Demo: Intelligent Recap")
+
+if not hasattr(st, 'global_state'):
+    st.global_state = {'NIPS 2021 Talks': None, 'TED Talks': None}
+    # NIPS 2021 Talks
+    transcript_files = itertools.islice(Path("demo_data/nips-2021/").rglob("transcript_whisper_large-v2.vtt"), 15)
+    # get titles from metadata.json
+    transcripts_map = {}
+    for transcript_file in transcript_files:
+        base_path = transcript_file.parent
+        metadata = base_path / "metadata.json"
+        txt_file = base_path / "transcript_whisper_large-v2.txt"
+        with open(metadata) as f:
+            metadata = json.load(f)
+            title = metadata["title"]
+            transcript = get_transcript(txt_file)
+            captions = get_captions_from_vtt(transcript_file)
+            transcripts_map[title] = {"transcript": transcript, "captions": captions, "video": base_path / "video.mp4"}
+    st.global_state['NIPS 2021 Talks'] = transcripts_map
+
+    data = pd.read_json("demo_data/ted_talks.json")
+    video_ids = data.talk_id.tolist()
+    transcripts = data.text.apply(lambda x: " ".join(x)).tolist()
+    transcripts_map = {}
+    for video_id, transcript in zip(video_ids, transcripts):
+        metadata = get_talk_metadata(video_id)
+        title = metadata["data"]["video"]["title"]
+        presenter = metadata["data"]["video"]["presenterDisplayName"]
+        print(metadata["data"])
+        if metadata["data"]["video"]["nativeDownloads"] is None:
+            continue
+        video_url = metadata["data"]["video"]["nativeDownloads"]["medium"]
+        transcripts_map[title] = {"transcript": transcript, "video": video_url, "presenter": presenter}
+    st.global_state['TED Talks'] = transcripts_map
+
+    def get_lecture_id(path):
+        return int(path.parts[-2].split('-')[1])
+
+    transcript_files = Path("demo_data/lectures/").rglob("English.vtt")
+    sorted_path_list = sorted(transcript_files, key=get_lecture_id)
+
+    transcripts_map = {}
+    for transcript_file in sorted_path_list:
+        base_path = transcript_file.parent
+        lecture_id = base_path.parts[-1]
+        transcript = " ".join([c["text"].strip() for c in get_captions_from_vtt(transcript_file)]).replace("\n", " ")
+        video_path = Path(base_path, "video.mp4")
+        transcripts_map["Machine Translation: " + lecture_id] = {"transcript": transcript, "video": video_path}
+    st.global_state['KIT Lectures'] = transcripts_map
+
+type_of_document = st.selectbox('What kind of document do you want to test it on?', list(st.global_state.keys()))
+
+transcripts_map = st.global_state[type_of_document]
+
+selected_talk = st.selectbox("Choose a document...", list(transcripts_map.keys()))
+
+st.video(str(transcripts_map[selected_talk]['video']), format="video/mp4", start_time=0)
+
+input_text = st.text_area("Transcript", value=transcripts_map[selected_talk]['transcript'], height=300)
+
+toc = Toc()
+
+summarization_todos = []
+
+with st.expander("Adjust Thresholds"):
+    threshold = st.slider('Chapter Segmentation Threshold', 0.00, 1.00, value=0.4, step=0.05)
+    paragraphing_threshold = st.slider('Paragraphing Threshold', 0.00, 1.00, value=0.5, step=0.05)
+
+if st.button("Process Transcript"):
+    with st.sidebar:
+        st.header("Table of Contents")
+        toc.placeholder()
+
+    st.header(selected_talk, divider='rainbow')
+    # if 'presenter' in transcripts_map[selected_talk]:
+    #     st.markdown(f"### *by **{transcripts_map[selected_talk]['presenter']}***")
+
+    captions = transcripts_map[selected_talk]['captions'] if 'captions' in transcripts_map[selected_talk] else None
+    result = client.segment(input_text, captions, generate_titles=True, threshold=threshold)
+    if USE_PARAGRAPHING_MODEL:
+        presult = paragrapher.segment(input_text, captions, generate_titles=False, threshold=paragraphing_threshold)
+        paragraphs = presult['segments']
+    segments, titles, sentences = result['segments'], result['titles'], result['sentences']
+
+    if USE_PARAGRAPHING_MODEL:
+        prev_chapter_idx = 0
+        prev_paragraph_idx = 0
+        segment = []
+        for i, sentence in enumerate(sentences):
+            chapter, chapter_idx = get_sublist_by_flattened_index(segments, i)
+            paragraph, paragraph_idx = get_sublist_by_flattened_index(paragraphs, i)
+
+            if (chapter_idx != prev_chapter_idx and paragraph_idx == prev_paragraph_idx) or (paragraph_idx != prev_paragraph_idx and chapter_idx != prev_chapter_idx):
+                print("Chapter / Chapter & Paragraph")
+                segment_text = " ".join(segment)
+                toc.subheader(titles[prev_chapter_idx])
+                if len(segment_text) > 1200:
+                    generated_text_box = st.info("")
+                    summarization_todos.append(partial(generate_summary, summarizer, generated_text_box, segment_text, BP_PROMPT, BP_MAX_INPUT_LENGTH, prefix="\u2022"))
+                elif len(segment_text) > 450:
+                    generated_text_box = st.info("")
+                    summarization_todos.append(partial(generate_summary, summarizer, generated_text_box, segment_text, TLDR_PROMPT, TLDR_MAX_INPUT_LENGTH))
+                st.write(segment_text)
+                segment = []
+            elif paragraph_idx != prev_paragraph_idx and chapter_idx == prev_chapter_idx:
+                print("Paragraph")
+                segment.append("\n\n")
+            
+            segment.append(sentence)
+
+            prev_chapter_idx = chapter_idx
+            prev_paragraph_idx = paragraph_idx
+
+        segment_text = " ".join(segment)
+        toc.subheader(titles[prev_chapter_idx])
+        if len(segment_text) > 1200:
+            generated_text_box = st.info("")
+            summarization_todos.append(partial(generate_summary, summarizer, generated_text_box, segment_text, BP_PROMPT, BP_MAX_INPUT_LENGTH, prefix="\u2022"))
+        elif len(segment_text) > 450:
+            generated_text_box = st.info("")
+            summarization_todos.append(partial(generate_summary, summarizer, generated_text_box, segment_text, TLDR_PROMPT, TLDR_MAX_INPUT_LENGTH))
+        st.write(segment_text)
+
+
+    else:
+        segments = [" ".join([sentence for sentence in segment]) for segment in segments]
+        for title, segment in zip(titles, segments):
+            toc.subheader(title)
+            if len(segment) > 1200:
+                generated_text_box = st.info("")
+                summarization_todos.append(partial(generate_summary, summarizer, generated_text_box, segment, BP_PROMPT, BP_MAX_INPUT_LENGTH, prefix="\u2022"))
+            elif len(segment) > 450:
+                generated_text_box = st.info("")
+                summarization_todos.append(partial(generate_summary, summarizer, generated_text_box, segment, TLDR_PROMPT, TLDR_MAX_INPUT_LENGTH))
+            st.write(segment)
+    toc.generate()
+
+for summarization_todo in summarization_todos:
+    summarization_todo()