extract_resource_covid.py

text_org = input("Enter text, or press return for an example: ")
import location
import pudb
from nltk.tokenize import word_tokenize 
if text_org == "":
    text_org = "Oxygen producing unit at Princess Esra Hospital (Owaisi Group of Hospitals). #Oxygen #IndiaNeedsOxygen #IndiaFightsCOVID19 @aimim_national @imShaukatAli @asadowaisi @imAkbarOwaisi  @warispathan @syedasimwaqar @Syed_Ruknuddin5 @ShahnawazAIMIM_ @Akhtaruliman5  https://t.co/vdZamB1wJl"
text = text_org.lower()

places = location.return_location_list(text)
each_loc = [place[0] for place in places]
resources = {
    "oxygen": "Oxygen",
    "o2": "Oxygen",
    "ventilator": "Ventilator",
    "bed": "Beds",
    "icu": "Beds",
    "remdes": "Remdesivir",
    "plasma": "Plasma",
    "consultation": "Doctor",
    "ambulance": "Ambulance"
}

# pu.db
tokenized_text = word_tokenize(text)
print("\nOrig tokenized text:" + str(tokenized_text))
for i in reversed(range(1, len(tokenized_text))):
    # pu.db
    word = tokenized_text[i]
    word_prev = tokenized_text[i - 1]
    if "#" in word_prev:
        del tokenized_text[i]

print("\nNew tokenized text:" + str(tokenized_text))
text = ""
for word in tokenized_text:
    text = text+word+" "

places_to_remove = []
resource_text = ""
for resource in resources:
    if resource in each_loc:
        places_to_remove.append(each_loc.index(resource))
    if resource in text:
        resource_text = resource_text+resources[resource]+" "

places_to_remove.sort(reverse=True)
for ptr in places_to_remove:
    del places[ptr]

resource_text = word_tokenize(resource_text)
resource_text = [w.lower() for w in resource_text]
resource_text = list(set(resource_text))
print("\n\n\nText: "+str(text_org))
print("\nLocation: "+str(places))
print("\nResources: "+str(resource_text))

if "availab" in text:
    print("\nType: Availability")
elif "need" in text or "require" in text:
    print("\nType: Need")
else:
    print("\nType: Other")