mirror of
https://github.com/Monadical-SAS/reflector.git
synced 2025-12-20 20:29:06 +00:00
95 lines
3.1 KiB
Python
95 lines
3.1 KiB
Python
import argparse
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import nltk
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nltk.download('stopwords')
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from nltk.corpus import stopwords
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from nltk.tokenize import word_tokenize, sent_tokenize
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from heapq import nlargest
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from loguru import logger
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# Function to initialize the argument parser
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def init_argparse():
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parser = argparse.ArgumentParser(
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usage="%(prog)s <TRANSCRIPT> <SUMMARY>",
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description="Summarization"
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)
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parser.add_argument("transcript", type=str, default="transcript.txt", help="Path to the input transcript file")
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parser.add_argument("summary", type=str, default="summary.txt", help="Path to the output summary file")
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parser.add_argument("--num_sentences", type=int, default=5, help="Number of sentences to include in the summary")
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return parser
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# Function to read the input transcript file
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def read_transcript(file_path):
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with open(file_path, "r") as file:
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transcript = file.read()
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return transcript
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# Function to preprocess the text by removing stop words and special characters
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def preprocess_text(text):
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stop_words = set(stopwords.words('english'))
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words = word_tokenize(text)
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words = [w.lower() for w in words if w.isalpha() and w.lower() not in stop_words]
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return words
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# Function to score each sentence based on the frequency of its words and return the top sentences
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def summarize_text(text, num_sentences):
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# Tokenize the text into sentences
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sentences = sent_tokenize(text)
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# Preprocess the text by removing stop words and special characters
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words = preprocess_text(text)
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# Calculate the frequency of each word in the text
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word_freq = nltk.FreqDist(words)
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# Calculate the score for each sentence based on the frequency of its words
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sentence_scores = {}
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for i, sentence in enumerate(sentences):
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sentence_words = preprocess_text(sentence)
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for word in sentence_words:
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if word in word_freq:
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if i not in sentence_scores:
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sentence_scores[i] = word_freq[word]
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else:
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sentence_scores[i] += word_freq[word]
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# Select the top sentences based on their scores
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top_sentences = nlargest(num_sentences, sentence_scores, key=sentence_scores.get)
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# Sort the top sentences in the order they appeared in the original text
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summary_sent = sorted(top_sentences)
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summary = [sentences[i] for i in summary_sent]
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return " ".join(summary)
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def main():
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# Initialize the argument parser and parse the arguments
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parser = init_argparse()
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args = parser.parse_args()
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# Read the input transcript file
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logger.info(f"Reading transcript from: {args.transcript}")
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transcript = read_transcript(args.transcript)
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# Summarize the transcript using the nltk library
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logger.info("Summarizing transcript")
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summary = summarize_text(transcript, args.num_sentences)
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# Write the summary to the output file
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logger.info(f"Writing summary to: {args.summary}")
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with open(args.summary, "w") as f:
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f.write("Summary of: " + args.transcript + "\n\n")
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f.write(summary)
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logger.info("Summarization completed")
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if __name__ == "__main__":
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main()
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