import streamlit as st import pandas as pd from PIL import Image import json from process_predict import process_predict import re # Enter your keys/secrets as strings in the following fields # authorization tokens credentials = {} credentials['CONSUMER_KEY'] = 'XV2kS4M1OmganL2zZU0q8Kyxh' credentials['CONSUMER_SECRET'] = 'PvjekJXnI304fE2En3cmYuftP7yOXH0xiANsWOsW1nUpbwV4j7' credentials['ACCESS_TOKEN'] = '152569292-Uw6KPJqudtctiYjpR1GEWOMYMKGc2DhczLiZq4Q4' credentials['ACCESS_SECRET'] = 'Muv9NC0JhKiskMqt7hO7XNbCZPRBOAOtADNaAN8xeBQ1a' # Save the credentials object to file with open("twitter_credentials.json", "w") as file: json.dump(credentials, file) from twython import Twython import json # Load credentials from json file with open("twitter_credentials.json", "r") as file: creds = json.load(file) # Instantiate an object python_tweets = Twython(creds['CONSUMER_KEY'], creds['CONSUMER_SECRET']) # DB Management import sqlite3 conn = sqlite3.connect('data.db') c = conn.cursor() # DB Functions def create_usertable(): c.execute('CREATE TABLE IF NOT EXISTS userstable(FirstName TEXT,LastName TEXT,Mobile TEXT,Email TEXT,password TEXT,Cpassword TEXT)') def add_userdata(FirstName,LastName,Mobile,Email,password,Cpassword): c.execute('INSERT INTO userstable(FirstName,LastName,Mobile,Email,password,Cpassword) VALUES (?,?,?,?,?,?)',(FirstName,LastName,Mobile,Email,password,Cpassword)) conn.commit() def login_user(Email,password): c.execute('SELECT * FROM userstable WHERE Email =? AND password = ?',(Email,password)) data = c.fetchall() return data def view_all_users(): c.execute('SELECT * FROM userstable') data = c.fetchall() return data def delete_user(Email): c.execute("DELETE FROM userstable WHERE Email="+"'"+Email+"'") conn.commit() def main(): st.title("Welcome To Crime User Prediction") menu = ["Home","Login","SignUp"] choice = st.sidebar.selectbox("Menu",menu) if choice == "Home": original_title="

Twitter is used extensively in the United States as well as globally, creating many opportunities to augment decision support systems with Twitter-driven predictive analytics. Twitter is an ideal data source for decision support: its users, who number in the millions, publicly discuss events, emotions, and innumerable other topics; its content is authored and distributed in real time at no charge; and individual messages (also known as tweets) are often tagged with precise spatial and temporal coordinates. This article presents research investigating the use of spatiotemporally tagged tweets for crime prediction.

" image = Image.open('flow.jpg') st.image(image) st.markdown(original_title, unsafe_allow_html=True) elif choice == "Login": st.subheader("Login Section") Email = st.sidebar.text_input("Email") password = st.sidebar.text_input("Password",type='password') if st.sidebar.checkbox("Login"): #Validation regex = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b' if re.fullmatch(regex, Email): create_usertable() if Email=='a@a.com' and password=='123': st.success("Logged In as {}".format("Admin")) Email=st.text_input("Delete Email") if st.button('Delete'): delete_user(Email) user_result = view_all_users() clean_db = pd.DataFrame(user_result,columns=["FirstName","LastName","Mobile","City","Email","password","Cpassword"]) st.dataframe(clean_db) else: result = login_user(Email,password) if result: location=['Gujarat','UP','Maharastra','Delhi'] choice = st.selectbox("Select Location",location) if choice=='Gujarat': geocode = '28.6517178,77.2219388,1000mi' # latitude,longitude,distance(mi/km) if choice=='UP': geocode = '28.6517178,77.2219388,1000mi' # latitude,longitude,distance(mi/km) if choice=='Maharastra': geocode = '28.6517178,77.2219388,1000mi' # latitude,longitude,distance(mi/km) if choice=='Delhi': geocode = '28.6517178,77.2219388,1000mi' # latitude,longitude,distance(mi/km) texts=str(st.text_input("Enter Keyword with AND and OR operator")) keywords=texts+" -filter:retweets" query = {'q': keywords, 'count': 100, 'lang': 'en', 'geocode': geocode, } if st.button('Retrive Tweets'): # Search tweets dict_ = {'user': [], 'date': [], 'text': [], 'user_loc': []} for status in python_tweets.search(**query)['statuses']: dict_['user'].append(status['user']['screen_name']) dict_['date'].append(status['created_at']) dict_['text'].append(status['text']) dict_['user_loc'].append(status['user']['location']) # Structure data in a pandas DataFrame for easier manipulation df = pd.DataFrame(dict_) st.dataframe(df) df1=process_predict(df) if st.button("Process and Predict"): df1=pd.read_csv("tweets.csv") st.dataframe(df1) else: st.warning("Incorrect Email/Password") else: st.warning("Not Valid Email") elif choice == "SignUp": FirstName = st.text_input("Firstname") LastName = st.text_input("Lastname") Mobile = st.text_input("Mobile") Email = st.text_input("Email") new_password = st.text_input("Password",type='password') Cpassword = st.text_input("Confirm Password",type='password') if st.button("Signup"): pattern=re.compile("(0|91)?[7-9][0-9]{9}") regex = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b' if (pattern.match(Mobile)): if re.fullmatch(regex, Email): create_usertable() add_userdata(FirstName,LastName,Mobile,Email,new_password,Cpassword) st.success("You have successfully created a valid Account") st.info("Go to Login Menu to login") else: st.warning("Not Valid Email") else: st.warning("Not Valid Mobile Number") if __name__ == '__main__': main()