Update app.py

app.py

@@ -37,7 +37,7 @@ from geopy.geocoders import Nominatim
 
 offset = None
 
-def get_data(bot_token: str) -> list[str]:
+def get_data(bot_token):
     global offset
     try:
         if offset == None:
@@ -66,7 +66,7 @@ def get_data(bot_token: str) -> list[str]:
 
 """# Classifier"""
 
-def classify_message(bot_token: str) -> Union[List[str], List[str]]:
+def classify_message(bot_token):
   error_msg = ['An error occurred. Possibly empty request result or your Telegram Bot Token is incorrect.']
   disaster_docs = []
   classifier = pipeline("sentiment-analysis", model="Madhana/disaster_msges_classifier_v1")
@@ -84,7 +84,7 @@ def classify_message(bot_token: str) -> Union[List[str], List[str]]:
 """# NER Pipeline"""
 
 @spacy.Language.component("disaster_ner")
-def disaster_ner(doc: spacy.tokens.Doc) -> spacy.tokens.Doc:
+def disaster_ner(doc):
     matcher = PhraseMatcher(doc.vocab)
     patterns = list(nlp.tokenizer.pipe(Tamil_words))
     matcher.add("Tamil_words", None, *patterns)
@@ -98,12 +98,12 @@ Tamil_words = ['மதனா பாலா'] # umm, that's my name in Tamil, cons
 nlp = spacy.load("en_pipeline")
 nlp.add_pipe("disaster_ner", name="disaster_ner", before='ner')
 
-def create_address(row: pd.Series) -> str:
+def create_address(row):
     return f"{row['STREET']}, {row['NEIGHBORHOOD']}, {row['CITY']}"
 
 geolocator = Nominatim(user_agent="disaster-ner-app")
 
-def geocode_address(address: str) -> tuple:
+def geocode_address(address):
     try:
         location = geolocator.geocode(address)
         return (location.latitude, location.longitude)
@@ -112,7 +112,7 @@ def geocode_address(address: str) -> tuple:
 
 """# With Classifier"""
 
-def get_classifier_ner(bot_token: str) -> pd.DataFrame:
+def get_classifier_ner(bot_token):
   data = classify_message(bot_token)
   entity_types = ["NAME", "STREET", "NEIGHBORHOOD", "CITY", "PHONE NUMBER","YO!"]
   df = pd.DataFrame(columns=["Text"] + entity_types)
@@ -139,7 +139,7 @@ def get_classifier_ner(bot_token: str) -> pd.DataFrame:
 
 """## Without Classifier"""
 
-def get_ner(bot_token: str) -> pd.DataFrame:
+def get_ner(bot_token):
   data = get_data(bot_token)
   entity_types = ["NAME", "STREET", "NEIGHBORHOOD", "CITY", "PHONE NUMBER","YO!"]
   df = pd.DataFrame(columns=["Text"] + entity_types)
@@ -167,10 +167,10 @@ def get_ner(bot_token: str) -> pd.DataFrame:
 
 """# Gradio"""
 
-def process_ner_data(your_bot_token) -> pd.DataFrame:
+def process_ner_data(your_bot_token):
     return get_ner(your_bot_token)
 
-def process_classifier_ner_data(your_bot_token) -> pd.DataFrame:
+def process_classifier_ner_data(your_bot_token):
     return get_classifier_ner(your_bot_token)
 
 demo = gr.Blocks()