Create scripture_compare.py

scripture_compare.py

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+import urllib.request, json
+from transformers.utils import logging
+logging.set_verbosity_error()
+
+from sentence_transformers import SentenceTransformer
+from sentence_transformers import util
+
+# Search URL for Mackabee Ministries scriptures. Will fetch scriptures as a json object.
+SEARCH_URL = 'https://dd4-biblical.appspot.com/_api/scriptures/v1/search?searchText={}&lang=en&version={}'
+STANDARD_VERSION = 'RSKJ' # Normally RSKJ (Restored King James Version) as our standard text, may also use NRSV.
+COMPARISON_VERSION = 'SEP' # Will use the Septuagint "SEP" version for most comparisons, also have DSS available for Isa
+
+
+# The list of reference scriptures to compare, this will be used in a search that can parse a lot of different reference
+# types
+REFERENCES = 'Gen 1'
+
+
+# Candidate helper class that holds a scripture, the comparison text and the resulting match score.
+class Candidate:
+    compareText = ''
+    score = 0
+
+    def __init__(self, book, chapter, verse, standardText):
+        self.book = book
+        self.chapter = chapter
+        self.verse = verse
+        self.standardText = standardText
+
+    def reference(self):
+        return '{} {}:{}'.format(self.book, self.chapter, self.verse)
+
+    def __str__(self):
+        return '{},{:.4f},"{}","{}"'.format(self.reference(), self.score, self.standardText, self.compareText)
+
+    def __repr__(self):
+        return str(self)
+
+def compare(model, candidate):
+    embeddings1 = model.encode(candidate.standardText, convert_to_tensor=True)
+    #print(embeddings1)
+    embeddings2 = model.encode(candidate.compareText, convert_to_tensor=True)
+    #print(embeddings2)
+
+    cosine_scores = util.cos_sim(embeddings1, embeddings2)
+    #print(cosine_scores)
+
+    candidate.score = cosine_scores[0][0]
+
+
+if __name__ == '__main__':
+
+    candidates = []
+    candidateMap = {}
+    # first connect to the api and get the standard translation
+    with urllib.request.urlopen(SEARCH_URL.format(urllib.parse.quote(REFERENCES), STANDARD_VERSION)) as url:
+        response = json.load(url)
+        # print(response)
+
+        for result in response['items']:
+            # print(result)
+            candidate = Candidate(result['book'], result['chapter'], result['verse'], result['text'])
+            candidates.append(candidate)
+            candidateMap[candidate.reference()] = candidate
+
+    # Then fetch the comparison text
+    with urllib.request.urlopen(SEARCH_URL.format(urllib.parse.quote(REFERENCES), COMPARISON_VERSION)) as url:
+        response = json.load(url)
+        # print(response)
+
+        for result in response['items']:
+            # print(result)
+            candidate = candidateMap['{} {}:{}'.format(result['book'], result['chapter'], result['verse'])]
+            if candidate is None :
+                candidate = Candidate(result['book'], result['chapter'], result['verse'], '')
+                candidates.append(candidate)
+                candidateMap[candidate.reference()] = candidate
+
+            candidate.compareText = result['text']
+
+    model = SentenceTransformer("all-MiniLM-L6-v2")
+
+    for candidate in candidates:
+        compare(model, candidate)
+        print(candidate)