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import pandas as pd |
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import numpy as np |
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import streamlit as st |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline |
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model_name = "deepset/roberta-base-squad2" |
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) |
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model = AutoModelForQuestionAnswering.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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suspicious_words = [ |
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"robbery", "crime", "exchange", "extortion", "threat", "suspicious", "fraud", "laundering", |
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"illegal", "contraband", "smuggling", "burglary", "assault", "hijacking", "kidnapping", "ransom", |
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"hostage", "terrorism", "homicide", "murder", "manslaughter", "weapon", "gun", "explosive", "bomb", "knives", |
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"threaten", "blackmail", "intimidate", "menace", "harassment", "stalking", "kidnap", "abduction", "guns", "bombs", |
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"abuse", "trafficking", "prostitution", "pimping", "drug", "narcotic", "cocaine", "heroin", "methamphetamine", |
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"amphetamine", "opiate", "meth", "gang", "gangster", "mafia", "racket", "extort", "embezzle", "corruption", |
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"bribe", "scam", "forgery", "counterfeit", "fraudulent", "cybercrime", "hacker", "phishing", "identity", "theft", |
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"credit card", "fraud", "identity", "fraud", "ponzi", "scheme", "pyramid", "scheme", "money", "scam", "swindle", "deception", |
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"conspiracy", "scheme", "plot", "coercion", "corrupt", "criminal", "felony", "misdemeanor", "felon", "fugitive", |
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"wanted", "arson", "arsonist", "arsony", "stolen", "steal", "loot", "heist", "launder", "hitman", "racketeer", |
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"hijack", "smuggle", "terrorist", "kidnapper", "perpetrator", "ringleader", "prowler", "vigilante", "sabotage", |
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"saboteur", "suicide", "discreet", "hide", "action", "profile", "alert", "vigilant", "clandestine", "riot", "arms", "deal" |
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] |
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q = ["","",""] |
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a = ["","",""] |
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q[0] = "What event is going to take place?" |
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q[1] = "Where is it going to happen" |
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q[2] = "What time is it going to happen?" |
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QA_input = [{} for i in range(3)] |
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res = [{} for i in range(3)] |
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df = pd.read_excel('senti.xlsx') |
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parsed_column = df['sentences'].to_list() |
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print(parsed_column) |
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for sentence in parsed_column: |
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for i in range(3): |
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QA_input[i] = { |
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'question': q[i], |
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'context': sentence |
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} |
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res[i] = nlp(QA_input[i]) |
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a[i] = res[i]['answer'] |
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a1 = a[0].lower() |
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a1s = set(a1.split()) |
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sus = set(suspicious_words) |
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cw = a1s.intersection(sus) |
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if len(cw) != 0: |
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st.write("The crime detected is: ",a[0]) |
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if len(a[1]) != 0: |
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st.write("The location of crime detected is: ",a[1]) |
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elif len(a[1]) == 0: |
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st.write("No location detected") |
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if len(a[2]) != 0: |
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st.write("The time of crime detected is: ",a[2]) |
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elif len(a[2]) == 0: |
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st.write("No time detected") |
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elif len(cw) == 0: |
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st.write("No crime detected") |
