Moved all server files to server/

server/reflector-local/2-agenda-transcript-diff.py

@@ -0,0 +1,68 @@
+import argparse
+
+import spacy
+from loguru import logger
+
+
+# Define the paths for agenda and transcription files
+def init_argparse() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        usage="%(prog)s <AGENDA> <TRANSCRIPTION>",
+        description="Compares the transcript of a video or audio file to an agenda using the SpaCy model"
+    )
+    parser.add_argument("agenda", help="Location of the agenda file")
+    parser.add_argument("transcription", help="Location of the transcription file")
+    return parser
+
+
+args = init_argparse().parse_args()
+agenda_path = args.agenda
+transcription_path = args.transcription
+
+# Load the spaCy model and add the sentencizer
+spaCy_model = "en_core_web_md"
+nlp = spacy.load(spaCy_model)
+nlp.add_pipe('sentencizer')
+logger.info("Loaded spaCy model " + spaCy_model)
+
+# Load the agenda
+with open(agenda_path, "r") as f:
+    agenda = [line.strip() for line in f.readlines() if line.strip()]
+logger.info("Loaded agenda items")
+
+# Load the transcription
+with open(transcription_path, "r") as f:
+    transcription = f.read()
+logger.info("Loaded transcription")
+
+# Tokenize the transcription using spaCy
+doc_transcription = nlp(transcription)
+logger.info("Tokenized transcription")
+
+# Find the items covered in the transcription
+covered_items = {}
+for item in agenda:
+    item_doc = nlp(item)
+    for sent in doc_transcription.sents:
+        if not sent or not all(token.has_vector for token in sent):
+            # Skip an empty span or one without any word vectors
+            continue
+        similarity = sent.similarity(item_doc)
+        similarity_threshold = 0.7
+        if similarity > similarity_threshold:  # Set the threshold to determine what is considered a match
+            covered_items[item] = True
+            break
+
+# Count the number of items covered and calculatre the percentage
+num_covered_items = sum(covered_items.values())
+percentage_covered = num_covered_items / len(agenda) * 100
+
+# Print the results
+print("💬 Agenda items covered in the transcription:")
+for item in agenda:
+    if item in covered_items and covered_items[item]:
+        print("✅ ", item)
+    else:
+        print("❌ ", item)
+print("📊 Coverage: {:.2f}%".format(percentage_covered))
+logger.info("Finished comparing agenda to transcription with similarity threshold of " + str(similarity_threshold))