Spaces:
Sleeping
Sleeping
File size: 6,319 Bytes
41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c a7288b8 41cfb5c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 |
# Importing the necessary libraries
import streamlit as st
import pandas as pd
import pickle
# Setting up the page configuration for Streamlit App
st.set_page_config(
page_title=" :mushroom: Mushroom App",
page_icon="🍄",
layout="wide",
initial_sidebar_state="expanded"
)
# Function for user input features
def user_input_features():
# Creating sliders and select boxes for user input in the sidebar
cap_diameter = st.sidebar.slider('Cap Diameter',
min_value=0.0,
max_value=2000.0,
value = 1000.0,
step=1.0,
)
cap_shape = st.sidebar.selectbox('Cap Shape',
options=('bell',
'conical',
'convex',
'flat',
'sunken',
'spherical',
'other',)
)
gill_attachment = st.sidebar.selectbox('Gill Attachment',
options=('adnate',
'adnexed',
'decurrent',
'free',
'sinuate',
'pores',
'none',)
)
gill_color = st.sidebar.selectbox('Gill Color',
options=('brown',
'buff',
'gray',
'green',
'pink',
'purple',
'red',
'white',
'yellow',
'blue',
'orange',
'black',)
)
stem_height = st.sidebar.slider('Stem Height',
min_value=0.0,
max_value=4.0,
value=2.0,
step=0.1,
)
stem_width = st.sidebar.slider('Stem Width',
min_value=0.0,
max_value=4000.0,
value=2000.0,
step=1.0,
)
stem_color = st.sidebar.selectbox('Stem Color',
options=('brown',
'buff',
'gray',
'green',
'pink',
'purple',
'red',
'white',
'yellow',
'blue',
'orange',
'black',)
)
season = st.sidebar.selectbox('Season',
options=('spring',
'summer',
'autumn',
'winter',)
)
# Function to get the color code
def get_color(color_name):
color_dict = {
'brown': 0,
'buff': 1,
'gray': 2,
'green': 3,
'pink': 4,
'purple': 5,
'red': 6,
'white': 7,
'yellow': 8,
'blue': 9,
'orange': 10,
'black': 11,
}
return color_dict.get(color_name.lower(), "not found")
# Function to get the cap shape code
def get_cap_shape(cap_shape):
shape_dict = {
'bell': 0,
'conical': 1,
'convex': 2,
'flat': 3,
'sunken': 4,
'spherical': 5,
'other': 6,
}
return shape_dict.get(cap_shape.lower(), "not found")
# Function to get gill attachment code
def get_gill_attachment(gill_attachment):
gill_attachment_dict = {
'adnate': 0,
'adnexed': 1,
'decurrent': 2,
'free': 3,
'sinuate': 4,
'pores': 5,
'none': 6,
}
return gill_attachment_dict.get(gill_attachment.lower(), "not found")
# Function to get season code
def get_season(season):
season_dict = {
'spring': 0,
'summer': 1,
'autumn': 2,
'winter': 3,
}
return season_dict.get(season.lower(), "not found")
# Creating a data dictionary to store the user input data
data = {'cap-diameter': cap_diameter,
'cap-shape': get_cap_shape(cap_shape),
'gill-attachment': get_gill_attachment(gill_attachment),
'gill-color': get_color(gill_color),
'stem-height': stem_height,
'stem-width': stem_width,
'stem-color': get_color(stem_color),
'season': get_season(season),
}
# Creating a DataFrame from the data dictionary
features = pd.DataFrame(data, index=[0])
return features
# Function to load the prediction model
#@st.cache_data()
def get_model():
model = pickle.load(open("models/rfc_model.pkl", "rb"))
return model
# Function to make prediction using the model and input data
def make_prediction(data):
model = get_model()
return model.predict(data)
# Main function
def main():
st.write("""# :mushroom: Mushroom App""")
st.sidebar.image("img/dataset-cover.jpg")
user_data = user_input_features()
# Creating a session state button for prediction
if 'btn_predict' not in st.session_state:
st.session_state['btn_predict'] = False
st.session_state['btn_predict'] = st.button("Predict")
# Making prediction and showing result
if st.session_state['btn_predict'] == True:
if make_prediction(user_data) == 1:
st.error("# Result: Poisonous :skull_and_crossbones: ")
else:
st.success("# Result: Edible :mushroom: ")
# Running the main function
if __name__ == "__main__":
main() |