import gradio as gr
import joblib
import pandas as pd

# Load the trained model and mappings
model = joblib.load('mushroom_classifier.pkl')
mappings = joblib.load('mappings.pkl')

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

def predict_mushroom(**features):
    numerical_features = {}
    for feature, value in features.items():
        if feature in mappings and value in mappings[feature]:
            numerical_features[feature] = mappings[feature][value]
        else:
            return "Invalid input detected."
    
    input_df = pd.DataFrame([numerical_features])
    prediction = model.predict(input_df)
    return "Edible" if prediction[0] == 0 else "Poisonous"

dropdowns = [gr.Dropdown(choices=list(options.values()), label=feature.replace('-', ' ').capitalize()) for feature, options in feature_options.items()]

demo = gr.Interface(fn=predict_mushroom, inputs=dropdowns, outputs="text", title="Mushroom Classification", description="Select the characteristics of the mushroom to determine if it's edible or poisonous.")

demo.launch()