Delete add_labels3.py

add_labels3.py

-import json
-
-json_data = {
-    "projectFileVersion": "2022-05-01",
-    "stringIndexType": "Utf16CodeUnit",
-    "metadata": {
-        "projectKind": "CustomEntityRecognition",
-        "storageInputContainerName": "example-data",
-        "projectName": "dcc-capstone",
-        "multilingual": "false",
-        "description": "Project-description",
-        "language": "en",
-        "settings": {}
-    },
-    "assets": {
-        "projectKind": "CustomEntityRecognition",
-        "entities": [
-            {"category": "positive"},
-            {"category": "negative"},
-            {"category": "percentage"},
-            {"category": "demographic"}
-        ],
-        "documents": []
-    }
-}
-for i in range(1, 7):
-    with open(str(i) + ".txt", 'r', encoding="utf-8") as f:
-        utf_data = f.read()
-        region_length = len(utf_data)
-        category = ""
-        offset = 0
-        length = 0
-        last_position = 0
-        is_a_category = False
-
-        document_structure = {
-            "location": str(i) + ".txt",
-            "language": "en-US",
-            "entities": [
-                {
-                    "regionOffset": 0,
-                    "regionLength": region_length,
-                    "labels": []
-                }
-            ]
-        }
-        for ind, word in enumerate(utf_data.split()):
-            if word.casefold().__contains__("positive") or word.casefold().__contains__("excite") or word.casefold().__contains__("benefit") or word.casefold().__contains__("beneficial") or word.casefold().__contains__("favor") or word.casefold().__contains__("good") or word.casefold().__contains__("optimist") or word.casefold().__contains__("favor"):
-                category = "positive"
-                offset = utf_data.find(word, last_position)
-                length = len(word)
-                last_position = offset + length
-                is_a_category = True
-            elif word.casefold().__contains__("negative") or word.casefold().__contains__("concern") or word.casefold().__contains__("harm") or word.casefold().__contains__("reject") or word.casefold().__contains__("oppose") or word.casefold().__contains__("danger") or word.casefold().__contains__("bad") or word.casefold().__contains__("pessimist") or word.casefold().__contains__("risk") or word.casefold().__contains__("threat"):
-                category = "negative"
-                offset = utf_data.find(word, last_position)
-                length = len(word)
-                last_position = offset + length
-                is_a_category = True
-            elif word.casefold().__contains__("%") or word.casefold().__contains__("half") or word.casefold().__contains__("third") or word.casefold().__contains__("fourth") or word.casefold().__contains__("fifth") or word.casefold().__contains__("tenth"):
-                category = "percentage"
-                offset = utf_data.find(word, last_position)
-                length = len(word)
-                last_position = offset + length
-                is_a_category = True
-            elif word.casefold().__contains__("American") or word.casefold().__contains__("white") or word.casefold().__contains__("black") or word.casefold().__contains__("asian") or word.casefold().__contains__("hispanic") or word.casefold().__contains__("women") or word.casefold().__contains__("men"):
-                category = "demographic"
-                offset = utf_data.find(word, last_position)
-                length = len(word)
-                last_position = offset + length
-                is_a_category = True
-
-            if is_a_category:
-                document_structure["entities"][0]["labels"].append({
-                    "category": category,
-                    "offset": offset,
-                    "length": length
-                })
-                is_a_category = False
-            
-        json_data["assets"]["documents"].append(document_structure)
-
-        # Move the writing of JSON data outside the loop
-        with open('labels.json', 'w') as file:
-            json.dump(json_data, file, indent=4)