llmware-ai--llmware
wehub-resource-sync
106 行
3.4 KiB
Python
106 行
3.4 KiB
Python
from llmware.library import Library
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from llmware.prompts import Prompt, Sources
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from llmware.retrieval import Query
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import streamlit as st
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import os
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import sys
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sys.path.insert(0, os.getcwd())
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from Utils import get_stored_files, get_stored_libraries
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ACCOUNT_NAME = 'lecture_tool'
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SUMMARIZER_MODEL = 'slim-summary-tool'
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#
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# Summarizes specified file in specified library.
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#
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# Performs a text query for the topic, which can be an empty string.
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#
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# Uses the SUMMARIZER_MODEL defined above to generate a summary.
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#
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@st.cache_data(show_spinner=False)
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def summarize_file(library_name, filename, topic):
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# Load in appropriate library
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library = Library().load_library(library_name, account_name=ACCOUNT_NAME)
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print('\nupdate: library card - ', library.get_library_card())
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# Create Query object
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query = Query(library)
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# Load in appropriate model
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summarizer_prompter = Prompt().load_model(SUMMARIZER_MODEL, temperature=0.0, sample=False)
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# Access all text blocks corresponding to specified file if no topic is provided
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if topic == '':
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# Filter out text chunks by filename
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print('\nupdate: no topic provided, summarizing entire library')
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query_results = query.apply_custom_filter(query.get_whole_library(), {'file_source': filename})
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# Change key in query results for compatibility with RAG call
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for result in query_results:
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result['text'] = result['text_search']
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del result['text_search']
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# Access the text blocks corresponding to the specified file and topic
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else:
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# Perform text query for the topic, then filter out text blocks by filename
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print('\nupdate: topic provided, performing text query')
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query_results = query.apply_custom_filter(query.text_query(topic), {'file_source': filename})
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print('\nupdate: correct library chunks - ', query_results)
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# Pass in appropriate text blocks as source to the model
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sources = Sources(summarizer_prompter).package_source(query_results, aggregate_source=True)
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print('\nupdate: sources - ', sources)
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# Prompt the model for a summary
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print('\nupdate: summarizing in process')
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response = summarizer_prompter.prompt_with_source('key points', first_source_only=False, verbose=True)
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print('\nupdate: response - ', response)
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# Create a list of only the unique points generated by the model
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key_points = []
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for resp in response:
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for point in resp["llm_response"]:
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if point not in key_points:
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if point.strip():
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if not point.strip().startswith("Not Found"):
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key_points.append('- ' + point)
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return key_points
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#
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# Main block for GUI logic.
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#
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if __name__ == '__main__':
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st.title('Summarize your lectures')
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st.write('### Prompt info')
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library_name = st.selectbox(
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'Select the library:',
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tuple(get_stored_libraries())
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)
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if library_name:
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filename = st.selectbox(
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'Select the file:',
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tuple(get_stored_files(library_name))
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)
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topic = st.text_input('Optionally enter a topic to summarize:')
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if (st.button('Summarize')):
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with st.spinner('Summarizing transcript... don\'t leave this page!'):
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response = summarize_file(library_name, filename, topic)
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st.write('### Summary')
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for point in response:
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st.write(point)
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