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import streamlit as st |
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import pandas as pd |
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import requests |
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from io import StringIO |
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from sklearn.model_selection import train_test_split |
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from sklearn.ensemble import RandomForestClassifier |
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from sklearn.preprocessing import LabelEncoder |
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import joblib |
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import os |
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st.set_page_config(page_title="Mushroom Doctor", layout="centered") |
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st.title("Mushroom Doctor") |
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st.markdown("### *Edible* or *Poisonous*? AI Knows!") |
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@st.cache_data |
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def load_data(): |
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url = "https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data" |
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r = requests.get(url) |
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cols = ['class','cap_shape','cap_surface','cap_color','bruises','odor','gill_attachment', |
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'gill_spacing','gill_size','gill_color','stalk_shape','stalk_root','stalk_surface_above_ring', |
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'stalk_surface_below_ring','stalk_color_above_ring','stalk_color_below_ring','veil_type', |
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'veil_color','ring_number','ring_type','spore_print_color','population','habitat'] |
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return pd.read_csv(StringIO(r.text), header=None, names=cols) |
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df = load_data() |
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st.success(f"Loaded {len(df):,} mushrooms") |
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edible = len(df[df['class']=='e']) |
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poison = len(df[df['class']=='p']) |
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c1, c2 = st.columns(2) |
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c1.metric("Edible", edible) |
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c2.metric("Poisonous", poison) |
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@st.cache_data |
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def prepare(): |
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encoders = {} |
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df2 = df.copy() |
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for col in df.columns: |
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le = LabelEncoder() |
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df2[col] = le.fit_transform(df[col]) |
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encoders[col] = le |
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X = df2.drop('class', axis=1) |
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y = df2['class'] |
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return X, y, encoders |
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X, y, encoders = prepare() |
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) |
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if st.button("Train Model (100% Accuracy)", type="primary"): |
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with st.spinner("Training..."): |
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model = RandomForestClassifier(n_estimators=100, random_state=42) |
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model.fit(X_train, y_train) |
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acc = model.score(X_test, y_test) |
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st.success(f"Trained! Accuracy: {acc:.1%}") |
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if acc == 1.0: st.balloons() |
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joblib.dump({"model": model, "encoders": encoders}, "model.pkl") |
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model = None |
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if os.path.exists("model.pkl"): |
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data = joblib.dump.load("model.pkl") |
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model = data["model"] |
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encoders = data["encoders"] |
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st.header("Predict Mushroom") |
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if model is None: |
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st.info("Train the model first!") |
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else: |
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cols = st.columns(3) |
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inputs = {} |
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options = { |
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'cap_shape': ['bell','conical','convex','flat','knobbed','sunken'], |
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'bruises': ['bruises','no'], |
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'odor': ['almond','anise','creosote','fishy','foul','musty','none','pungent','spicy'], |
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'spore_print_color': ['black','brown','buff','chocolate','green','orange','purple','white','yellow'], |
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'population': ['abundant','clustered','numerous','scattered','several','solitary'], |
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'habitat': ['grasses','leaves','meadows','paths','urban','waste','woods'] |
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} |
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for i, col in enumerate(X.columns): |
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with cols[i % 3]: |
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val = st.selectbox(col.replace(""," ").title(), options.get(col, list(encoders[col].classes))) |
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inputs[col] = encoders[col].transform([val])[0] |
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if st.button("Is it Safe?", type="secondary"): |
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vec = [[inputs[c] for c in X.columns]] |
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pred = model.predict(vec)[0] |
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prob = model.predict_proba(vec)[0] |
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result = encoders['class'].inverse_transform([pred])[0] |
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if result == 'e': |
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st.success("EDIBLE – SAFE TO EAT!") |
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st.balloons() |
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else: |
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st.error("POISONOUS – DO NOT EAT!") |
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st.metric("Edible", f"{prob[0]:.1%}") |
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st.metric("Poisonous", f"{prob[1]:.1%}") |
