m. polinsky
commited on
Update streamlit_app.py
Browse files- streamlit_app.py +58 -62
streamlit_app.py
CHANGED
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@@ -15,10 +15,7 @@ from source import Source
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from scrape_sources import NPRLite, CNNText, stub
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import random
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# can force rescrape
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# This may take a config to get sources as input
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@st.cache()
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def initialize(limit, rando, use_cache=True):
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clusters: dict[str:List[namedtuple]] = dict()
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# This is a container for the source classes.
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@@ -157,7 +154,7 @@ def ner_results(ner_object, groups=True, NER_THRESHOLD=0.5) -> List[str]:
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NER_API_URL = "https://api-inference.huggingface.co/models/dbmdz/bert-large-cased-finetuned-conll03-english"
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headers = {"Authorization": f"""Bearer {st.secrets['ato']}"""}
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LIMIT =
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USE_CACHE = True
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if not USE_CACHE:
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@@ -176,60 +173,59 @@ article_dict, clusters = initialize(LIMIT, USE_CACHE)
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# We now have clusters and cluster data. Redundancy.
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# We call a display function and get the user input.
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# For this its still streamlit.
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loop_control = input('Y to continue...')
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from scrape_sources import NPRLite, CNNText, stub
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import random
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def initialize(limit, rando, use_cache=True):
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clusters: dict[str:List[namedtuple]] = dict()
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# This is a container for the source classes.
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NER_API_URL = "https://api-inference.huggingface.co/models/dbmdz/bert-large-cased-finetuned-conll03-english"
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headers = {"Authorization": f"""Bearer {st.secrets['ato']}"""}
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LIMIT = 20 # Controls time and number of clusters.
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USE_CACHE = True
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if not USE_CACHE:
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# We now have clusters and cluster data. Redundancy.
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# We call a display function and get the user input.
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# For this its still streamlit.
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selections = []
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choices = list(clusters.keys())
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choices.insert(0,'None')
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# Form used to take 3 menu inputs
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with st.form(key='columns_in_form'):
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cols = st.columns(3)
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for i, col in enumerate(cols):
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selections.append(col.selectbox(f'Make a Selection', choices, key=i))
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submitted = st.form_submit_button('Submit')
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if submitted:
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selections = [i for i in selections if i is not None]
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with st.spinner(text="Digesting...please wait, this will take a few moments...Maybe check some messages or start reading the latest papers on summarization with transformers...."):
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found = False
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# Check if we already have this digest.
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for i in digests:
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if set(selections) == set(list(i)):
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digestor = digests[i]
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found = True
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break
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# If we need a new digest
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if not found:
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chosen = []
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# Why not just use answers.values()?
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for i in selections: # i is supposed to be a list of stubs, mostly one
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if i != 'None':
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for j in clusters[i]:
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if j not in chosen:
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chosen.append(j) # j is supposed to be a stub.
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# Article dict contains stubs for unprocessed articles and lists of summarized chunks for processed ones.
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# Here we put together a list of article stubs and/or summary chunks and let the digestor sort out what it does with them,
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chosen = [i if isinstance(article_dict[i.hed], stub) else article_dict[i.hed] for i in chosen]
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# Digestor uses 'chosen', passed through 'stubs' to create digest.
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# 'user_choicese' is passed for reference.
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# Passing list(answers.values()) includes 'None' choices.
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digestor = Digestor(timer=Timer(), cache = USE_CACHE, stubs=chosen, user_choices=list(selections))
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# happens internally but may be used differently so it isn't automatic upon digestor creation.
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# Easily turn caching off for testing.
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digestor.digest() # creates summaries and stores them associated with the digest
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# Get displayable digest and digest data
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digestor.build_digest()# only returns for data collection
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digests[tuple(digestor.user_choices)] = digestor
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if len(digestor.text) == 0:
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st.write("You didn't select a topic!")
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else:
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st.write("Your digest is ready:\n")
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st.write(digestor.text)
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