Moved all server files to server/

server/reflector-local/3-transcript-summarizer.py

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