Upload app.py

app.py

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+# 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)
+
+# Function to process uploaded CSV file and make predictions
+def process_file(file):
+    data = pd.read_csv(file)
+    model = get_model()
+    predictions = model.predict(data)
+    data['prediction'] = predictions
+    return 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: ")
+
+    # File upload for batch prediction
+    st.write("## Batch Prediction")
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+    if uploaded_file is not None:
+        result_df = process_file(uploaded_file)
+        st.write(result_df)
+        csv = result_df.to_csv(index=False).encode('utf-8')
+        st.download_button(
+            label="Download predictions as CSV",
+            data=csv,
+            file_name='predictions.csv',
+            mime='text/csv',
+        )
+
+# Running the main function
+if __name__ == "__main__":
+    main()