app.py

import streamlit as st
import pickle
import numpy as np
from cassandra.cluster import Cluster
from cassandra.auth import PlainTextAuthProvider
import json
from uuid import uuid1
cloud_config= {
  'secure_connect_bundle': 'secure-connect-db-mushroom.zip'
}
with open("../db_mushroom-token.json") as f:
    secrets = json.load(f)

CLIENT_ID = secrets["clientId"]
CLIENT_SECRET = secrets["secret"]

auth_provider = PlainTextAuthProvider(CLIENT_ID, CLIENT_SECRET)
cluster = Cluster(cloud=cloud_config, auth_provider=auth_provider)
session = cluster.connect('keyspace_mushroom')

with open('mushroom_classification.pkl','rb') as f:
    model = pickle.load(f)

st.title("Mushroom Classification")
st.write("This is a Mushroom classification model that can predict 'Weather the mushroom is poisonous or Edible.'")

text = '''cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s
cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s
cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r,pink=p,purple=u,red=e,white=w,yellow=y
bruises: bruises=t,no=f
odor: almond=a,anise=l,creosote=c,fishy=y,foul=f,musty=m,none=n,pungent=p,spicy=s
gill-attachment: attached=a,descending=d,free=f,notched=n
gill-spacing: close=c,crowded=w,distant=d
gill-size: broad=b,narrow=n
gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e,white=w,yellow=y
stalk-shape: enlarging=e,tapering=t
stalk-root: bulbous=b,club=c,cup=u,equal=e,rhizomorphs=z,rooted=r,missing=?
stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s
stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s
stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o,pink=p,red=e,white=w,yellow=y
stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o,pink=p,red=e,white=w,yellow=y
veil-type: partial=p,universal=u
veil-color: brown=n,orange=o,white=w,yellow=y
ring-number: none=n,one=o,two=t
ring-type: cobwebby=c,evanescent=e,flaring=f,large=l,none=n,pendant=p,sheathing=s,zone=z
spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r,orange=o,purple=u,white=w,yellow=y
population: abundant=a,clustered=c,numerous=n,scattered=s,several=v,solitary=y
habitat: grasses=g,leaves=l,meadows=m,paths=p,urban=u,waste=w,woods=d'''
key = 1
dic={}
for line in text.split("\n"):
    feature = line.split(": ")
    
    sub_dic={}
    count=1
    for each in feature[1].split(","):
        sub_key = each.split("=")[0]
        if sub_key not in sub_dic:
            sub_dic[sub_key]=count
            count+=1
    if key not in dic:
        dic[key]=sub_dic
    key+=1
# print(dic)



arr=[]
new_arr =[]
count=1
for text in text.split('\n'):
    split = text.split(": ")
    head = split[0]
    tail = split[1].split(',')
    for i in range(len(tail)):
        tail[i] = tail[i][:-2]
    ans = st.selectbox(head, tail)
    for token in split[1].split(','):
        tokenizer = token.split('=')
        if tokenizer[0] == ans:
            new_arr.append(tokenizer[1])
    # print(((dic[count][ans] - 1)*(1-0)/(len(dic[count])-1)))
    arr.append(((dic[count][ans] - 1)*(1-0))/(len(dic[count])-1))
    count+=1
print(new_arr)
print(">>>>>>>>>>>>>")


def prediction(arr):
    arr = np.array(arr).reshape(-1,)
    output = model.predict([arr])
    if output[0]==1.0:
        st.write("## The Mushroom is Edible")
        pred_class = 'e'
    else:
        st.write("## The Mushroom is Poisonous")
        pred_class = 'p'
    return pred_class
    
if st.button("submit"):
    pred_class = prediction(arr)
    new_arr.insert(0, pred_class)
    column_names=['class','cap_shape','cap_surface','cap_color','bruises','odor','gill_attachment','gill_spacing','gill_size','gill_color','stalk_shape','stalk_root','stalk_surface_above_ring','stalk_surface_below_ring','stalk_color_above_ring','stalk_color_below_ring','veil_type','veil_color','ring_number','ring_type','spore_print_color','population','habitat']
    column_names=sorted(column_names)
    column_names.insert(0,'id')
    new_arr.insert(0,uuid1())
    insert_query = f"INSERT INTO mushrooms ({', '.join(column_names)}) VALUES ({', '.join(['?'] * len(column_names))})"

    prepared_statement = session.prepare(insert_query)
    bound_statement = prepared_statement.bind(new_arr)
    session.execute(bound_statement)

    
    print(new_arr)