Create app.py

app.py

@@ -0,0 +1,97 @@
+import os
+from flask import Flask, request, jsonify
+import spacy
+import re
+
+# Set environment variables for writable directories
+os.environ['TRANSFORMERS_CACHE'] = '/tmp/transformers_cache'
+os.environ['MPLCONFIGDIR'] = '/tmp/.matplotlib'
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Load the spaCy models once
+nlp = spacy.load("en_core_web_sm")
+nlp_coref = spacy.load("en_coreference_web_trf")
+
+REPLACE_PRONOUNS = {"he", "she", "they", "He", "She", "They"}
+
+def extract_core_name(mention_text, main_characters):
+    words = mention_text.split()
+    for character in main_characters:
+        if character.lower() in mention_text.lower():
+            return character
+    return words[-1]
+
+def calculate_pronoun_density(text):
+    doc = nlp(text)
+    pronoun_count = sum(1 for token in doc if token.pos_ == "PRON" and token.text in REPLACE_PRONOUNS)
+    named_entity_count = sum(1 for ent in doc.ents if ent.label_ == "PERSON")
+    return pronoun_count / max(named_entity_count, 1), named_entity_count
+
+def resolve_coreferences_across_text(text, main_characters):
+    doc = nlp_coref(text)
+    coref_mapping = {}
+    for key, cluster in doc.spans.items():
+        if re.match(r"coref_clusters_*", key):
+            main_mention = cluster[0]
+            core_name = extract_core_name(main_mention.text, main_characters)
+            if core_name in main_characters:
+                for mention in cluster:
+                    for token in mention:
+                        if token.text in REPLACE_PRONOUNS:
+                            core_name_final = core_name if token.text.istitle() else core_name.lower()
+                            coref_mapping[token.i] = core_name_final
+    resolved_tokens = []
+    current_sentence_characters = set()
+    current_sentence = []
+    for i, token in enumerate(doc):
+        if token.is_sent_start and current_sentence:
+            resolved_tokens.extend(current_sentence)
+            current_sentence_characters.clear()
+            current_sentence = []
+        if i in coref_mapping:
+            core_name = coref_mapping[i]
+            if core_name not in current_sentence_characters and core_name.lower() not in [t.lower() for t in current_sentence]:
+                current_sentence.append(core_name)
+                current_sentence_characters.add(core_name)
+            else:
+                current_sentence.append(token.text)
+        else:
+            current_sentence.append(token.text)
+    resolved_tokens.extend(current_sentence)
+    resolved_text = " ".join(resolved_tokens)
+    return remove_consecutive_duplicate_phrases(resolved_text)
+
+def remove_consecutive_duplicate_phrases(text):
+    words = text.split()
+    i = 0
+    while i < len(words) - 1:
+        j = i + 1
+        while j < len(words):
+            if words[i:j] == words[j:j + (j - i)]:
+                del words[j:j + (j - i)]
+            else:
+                j += 1
+        i += 1
+    return " ".join(words)
+
+def process_text(text, main_characters):
+    pronoun_density, named_entity_count = calculate_pronoun_density(text)
+    min_named_entities = len(main_characters)
+    if pronoun_density > 0:
+        return resolve_coreferences_across_text(text, main_characters)
+    else:
+        return text
+
+# API endpoint to handle coreference resolution
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.json
+    text = data.get('text')
+    main_characters = data.get('main_characters')
+    resolved_text = process_text(text, main_characters.split(","))
+    return jsonify({"resolved_text": resolved_text})
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=int(os.getenv("PORT", 7860)))