Update home.py

home.py

@@ -3,51 +3,57 @@ import pickle
 import numpy as np
 import os
 
-# Load the trained model
-MODEL_PATH = "/home/user/app/wine_quality_model.pkl"  # Update with your actual model path
+# Load all trained models
+MODEL_FILES = {
+    "KNN": "knn_model.pkl",
+    "Random Forest": "random_forest_model.pkl",
+    "Decision Tree": "decision_tree_model.pkl",
+    "Bagging": "bagging_model.pkl",
+    "Voting": "voting_model.pkl",
+}
 
-# Streamlit UI
+# Streamlit UI Config
 st.set_page_config(page_title="🍷 Wine Quality Predictor", layout="centered")
 
-# Custom Styling for Background and Text
+# Custom Styling for Background & UI
 st.markdown(
     """
     <style>
     .stApp {
-        background-color: #003366;
+        background: linear-gradient(to right, #4B0101, #800020);
         color: white;
     }
     .title {
         font-size: 36px !important;
         font-weight: bold;
-        color: white;
+        color: #FFD700; /* Gold */
         text-align: center;
     }
     .subtitle {
         font-size: 24px !important;
         font-weight: bold;
-        color: #ffcc00;
+        color: #FFA500; /* Orange */
     }
-    .stSlider label, .stNumberInput label {
-        font-size: 20px !important;
+    .stSelectbox label, .stSlider label, .stNumberInput label {
+        font-size: 18px !important;
         font-weight: bold;
         color: white;
     }
     .stButton>button {
-        background-color: #ffcc00;
-        color: #003366;
+        background-color: #FFD700; /* Gold */
+        color: #4B0101; /* Wine Red */
         font-size: 18px;
         font-weight: bold;
         border-radius: 10px;
     }
     .stButton>button:hover {
-        background-color: #ff9900;
+        background-color: #FFA500; /* Orange */
         color: white;
     }
     .prediction {
         font-size: 26px;
         font-weight: bold;
-        color: #32CD32;
+        color: #32CD32; /* Bright Green */
         text-align: center;
     }
     </style>
@@ -55,34 +61,25 @@ st.markdown(
     unsafe_allow_html=True,
 )
 
+# Title and Description
 st.markdown('<h1 class="title">🍷 Wine Quality Prediction</h1>', unsafe_allow_html=True)
-st.write("Predict the quality of wine based on its properties.")
+st.write("Predict the quality of wine based on its chemical properties.")
+
+# Select Model
+st.markdown('<h2 class="subtitle">🔍 Select Prediction Model</h2>', unsafe_allow_html=True)
+selected_model = st.selectbox("Choose a Model", list(MODEL_FILES.keys()))
 
-# Check if model file exists
-if os.path.exists(MODEL_PATH):
-    with open(MODEL_PATH, "rb") as f:
+# Load Selected Model
+model_path = MODEL_FILES[selected_model]
+if os.path.exists(model_path):
+    with open(model_path, "rb") as f:
         model = pickle.load(f)
     model_loaded = True
 else:
     model_loaded = False
-    st.error(f"Model file '{MODEL_PATH}' not found. Please check your uploaded files.")
-
-# User Inputs
-alcohol = st.slider("Alcohol Content", 8.0, 15.0, 10.0)
-volatile_acidity = st.slider("Volatile Acidity", 0.1, 1.5, 0.5)
-citric_acid = st.slider("Citric Acid", 0.0, 1.0, 0.3)
-sulphates = st.slider("Sulphates", 0.3, 2.0, 0.8)
-pH = st.slider("pH Level", 2.5, 4.0, 3.2)
-
-# Prepare input for model
-input_data = np.array([[alcohol, volatile_acidity, citric_acid, sulphates, pH]])
-
-# Prediction
-if st.button("Predict Quality"):
-    if model_loaded:
-        prediction = model.predict(input_data)
-        st.markdown(f'<p class="prediction">Predicted Wine Quality Score: {prediction[0]:.1f}</p>', unsafe_allow_html=True)
-    else:
-        st.error(f"Model file '{MODEL_PATH}' not found. Please upload the model file and try again.")
+    st.error(f"Model file '{model_path}' not found. Please upload the correct model file.")
 
-st.write("*Powered by Machine Learning*")
+# User Inputs for Wine Features
+st.markdown('<h2 class="subtitle">🍷 Enter Wine Properties</h2>', unsafe_allow_html=True)
+fixed_acidity = st.number_input("Fixed Acidity", min_value=3.0, max_value=15.0, value=7.0)
+volatile_acidity = st.number_input("