Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,4 +1,4 @@
-# streamlit_app.py  ← THIS NAME WORKS PERFECTLY ON HUGGING FACE
+# streamlit_app.py - Mushroom Classification App
 import streamlit as st
 import pandas as pd
 import requests
@@ -10,101 +10,171 @@ import joblib
 import os
 
 st.set_page_config(page_title="Mushroom Doctor", layout="centered")
-st.title("Mushroom Doctor")
-st.markdown("### Is it *Edible* or *Poisonous*?")
+st.title("🍄 Mushroom Doctor")
+st.markdown("### *Edible* or *Poisonous*? AI Will Tell You Instantly!")
 
-# Load dataset
+# Load Dataset Automatically
 @st.cache_data
-def load_data():
+def load_mushroom_data():
     url = "https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data"
-    r = requests.get(url)
-    cols = ['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']
-    return pd.read_csv(StringIO(r.text), header=None, names=cols)
+    response = requests.get(url)
+    if response.status_code == 200:
+        columns = [
+            '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'
+        ]
+        df = pd.read_csv(StringIO(response.text), header=None, names=columns)
+        return df
+    else:
+        st.error("Failed to load dataset.")
+        return None
 
-df = load_data()
-st.success("Dataset loaded – 8,124 mushrooms!")
+df = load_mushroom_data()
 
-# Show stats
-edible = len(df[df['class'] == 'e'])
-poisonous = len(df[df['class'] == 'p'])
-c1, c2 = st.columns(2)
-c1.metric("Edible (Safe)", edible)
-c2.metric("Poisonous (Deadly)", poisonous)
+if df is None:
+    st.stop()
 
-# Preprocess
+st.success(f"✅ Dataset loaded: {df.shape[0]:,} mushrooms analyzed")
+
+# Display Stats
+st.subheader("Dataset Overview")
+col1, col2 = st.columns(2)
+edible_count = len(df[df['class'] == 'e'])
+poisonous_count = len(df[df['class'] == 'p'])
+col1.metric("🍄 Edible Mushrooms", edible_count)
+col2.metric("☠ Poisonous Mushrooms", poisonous_count)
+
+st.dataframe(df.head(5), use_container_width=True)
+
+# Preprocess Data
 @st.cache_data
-def preprocess():
-    encoders = {}
-    df_enc = df.copy()
-    for col in df.columns:
+def preprocess_data(df):
+    le_dict = {}
+    df_encoded = df.copy()
+    for column in df.columns:
         le = LabelEncoder()
-        df_enc[col] = le.fit_transform(df[col])
-        encoders[col] = le
-    X = df_enc.drop('class', axis=1)
-    y = df_enc['class']
-    return X, y, encoders
+        df_encoded[column] = le.fit_transform(df[column])
+        le_dict[column] = le
+    X = df_encoded.drop('class', axis=1)
+    y = df_encoded['class']
+    return X, y, le_dict, df_encoded
 
-X, y, encoders = preprocess()
+X, y, label_encoders, df_encoded = preprocess_data(df)
+
+# Train-Test Split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
 
-# Train button
-if st.button("Train Model – 100% Accuracy!", type="primary"):
-    with st.spinner("Training Random Forest..."):
+# Train Model
+st.header("Train the Model")
+if st.button("🚀 Train Random Forest Model (Achieves 100% Accuracy!)"):
+    with st.spinner("Training the model..."):
         model = RandomForestClassifier(n_estimators=100, random_state=42)
         model.fit(X_train, y_train)
-        acc = model.score(X_test, y_test)
-        st.success(f"Trained! Accuracy: {acc:.1%}")
-        if acc == 1.0:
+        
+        # Evaluate
+        train_acc = model.score(X_train, y_train)
+        test_acc = model.score(X_test, y_test)
+        
+        st.success(f"✅ Model Trained Successfully!")
+        st.info(f"Training Accuracy: {train_acc:.4f} | Test Accuracy: {test_acc:.4f}")
+        
+        if test_acc == 1.0:
             st.balloons()
-            st.markdown("*PERFECT CLASSIFICATION!*")
-        joblib.dump({"model": model, "encoders": encoders}, "model.pkl")
+            st.markdown("🎉 PERFECT! 100% Classification Accuracy**")
+        
+        # Save Model
+        model_data = {
+            'model': model,
+            'label_encoders': label_encoders,
+            'features': X.columns.tolist()
+        }
+        joblib.dump(model_data, 'mushroom_model.pkl')
+        st.session_state.model_trained = True
 
-# Load model
-model = None
-if os.path.exists("model.pkl"):
-    loaded = joblib.load("model.pkl")
-    model = loaded["model"]
-    encoders = loaded["encoders"]
+# Load Trained Model
+st.header("Load Trained Model")
+if 'model_trained' not in st.session_state:
+    if os.path.exists('mushroom_model.pkl'):
+        try:
+            model_data = joblib.load('mushroom_model.pkl')
+            st.session_state.model = model_data['model']
+            st.session_state.label_encoders = model_data['label_encoders']
+            st.session_state.feature_names = model_data['features']
+            st.session_state.model_trained = True
+            st.success("✅ Model loaded from file!")
+        except Exception as e:
+            st.error(f"Error loading model: {e}")
+    else:
+        st.info("Train the model first or it will be created on first prediction.")
 
-# Prediction
-st.header("Check Your Mushroom")
-if model is None:
-    st.info("Click 'Train Model' first!")
+# Prediction Section
+st.header("🧪 Predict: Is This Mushroom Safe?")
+if 'model' not in st.session_state:
+    st.info("👆 Train or load the model above to make predictions!")
 else:
+    model = st.session_state.model
+    encoders = st.session_state.label_encoders
+    features = st.session_state.feature_names
+    
+    # Feature Selection UI
+    st.subheader("Select Mushroom Features")
+    input_features = {}
     cols = st.columns(3)
-    inputs = {}
+    
+    # Simplified feature options for UI (key features only)
     feature_options = {
-        'odor': ['none','almond','anise','creosote','fishy','foul','musty','pungent','spicy'],
-        'bruises': ['bruises','no'],
-        'gill_size': ['broad','narrow'],
-        'spore_print_color': ['black','brown','buff','chocolate','green','orange','purple','white','yellow'],
-        'population': ['abundant','clustered','numerous','scattered','several','solitary'],
-        'habitat': ['grasses','leaves','meadows','paths','urban','waste','woods']
+        'cap_shape': ['bell', 'conical', 'convex', 'flat', 'knobbed', 'sunken'],
+        'cap_surface': ['fibrous', 'grooves', 'smooth', 'scaly'],
+        'cap_color': ['buff', 'cinnamon', 'red', 'gray', 'brown', 'pink', 'green', 'purple', 'white', 'yellow'],
+        'bruises': ['yes', 'no'],
+        'odor': ['almond', 'creosote', 'foul', 'anise', 'musty', 'none', 'pungent', 'spicy', 'fishy'],
+        'gill_color': ['buff', 'red', 'gray', 'chocolate', 'black', 'brown', 'orange', 'pink', 'green', 'purple', 'white', 'yellow'],
+        'stalk_shape': ['enlarging', 'tapering'],
+        'stalk_root': ['bulbous', 'club', 'equal', 'rooted', '?'],
+        'spore_print_color': ['black', 'brown', 'buff', 'chocolate', 'green', 'orange', 'purple', 'white', 'yellow'],
+        'population': ['abundant', 'clustered', 'numerous', 'scattered', 'several', 'solitary'],
+        'habitat': ['woods', 'grasses', 'leaves', 'meadows', 'paths', 'urban', 'waste']
     }
     
-    for i, col in enumerate(X.columns):
+    for i, feat in enumerate(features):
         with cols[i % 3]:
-            options = feature_options.get(col, list(encoders[col].classes_))
-            val = st.selectbox(col.replace("_", " ").title(), options, key=col)
-            inputs[col] = encoders[col].transform([val])[0]
-
-    if st.button("Predict – Safe or Deadly?", type="secondary"):
-        input_vec = [[inputs[c] for c in X.columns]]
-        pred = model.predict(input_vec)[0]
-        prob = model.predict_proba(input_vec)[0]
-        result = encoders['class'].inverse_transform([pred])[0]
-
-        if result == 'e':
-            st.success("EDIBLE – SAFE TO EAT!")
+            if feat in feature_options:
+                options = feature_options[feat]
+            else:
+                options = list(encoders[feat].classes_)
+            selected = st.selectbox(f"{feat.replace('_', ' ').title()}", options, key=feat)
+            encoded_val = encoders[feat].transform([selected])[0]
+            input_features[feat] = encoded_val
+    
+    # Make Prediction
+    if st.button("🔮 Predict Safety", type="secondary"):
+        # Prepare input
+        input_df = pd.DataFrame([input_features])[features]  # Ensure column order
+        
+        # Predict
+        prediction = model.predict(input_df)[0]
+        probabilities = model.predict_proba(input_df)[0]
+        
+        # Decode prediction
+        predicted_class = encoders['class'].inverse_transform([prediction])[0]
+        edible_prob = probabilities[0] if predicted_class == 'e' else probabilities[1]
+        poisonous_prob = 1 - edible_prob
+        
+        # Display Results
+        if predicted_class == 'e':
+            st.success("🍄 EDIBLE – SAFE TO EAT!")
             st.balloons()
         else:
-            st.error("POISONOUS – DO NOT EAT!")
-            st.warning("This mushroom is deadly!")
-
-        st.metric("Edible Probability", f"{prob[0]:.1%}")
-        st.metric("Poisonous Probability", f"{prob[1]:.1%}")
+            st.error("☠ POISONOUS – DO NOT EAT!")
+            st.warning("This mushroom could be dangerous or fatal.")
+        
+        col1, col2 = st.columns(2)
+        col1.metric("Edible Probability", f"{edible_prob:.1%}")
+        col2.metric("Poisonous Probability", f"{poisonous_prob:.1%}")
 
-st.caption("Mushroom Doctor • 100% Deployable • File: streamlit_app.py")
+st.markdown("---")
+st.caption("🍄 Mushroom Doctor | Powered by UCI Dataset & Random Forest | Built with Streamlit")