Update app.py

app.py

@@ -1,29 +1,24 @@
 import streamlit as st
 import pickle
-import pandas as pd
 import numpy as np
 import os
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import StandardScaler
 
-# Load the trained pipeline model
-MODEL_FILES = {
-    "Random Forest": "random_forest_pipeline.pkl",  # Ensure correct paths
-    "Decision Tree": "decision_tree_pipeline.pkl",
-    "KNN": "knn_pipeline.pkl",
-    "Bagging": "bagging_pipeline.pkl",
-    "Voting": "voting_pipeline.pkl",
-}
+# Define Model File Paths
+MODEL_PATH = "random_forest_pipeline.pkl"  
 
 # Streamlit UI Setup
 st.set_page_config(page_title="Wine Quality Prediction 🍷🔬", layout="centered")
 
-# Custom Styling
+# UI Styling
 st.markdown(
     """
     <style>
     .stApp { background-color: #003366; color: white; }
-    .title { font-size: 36px !important; font-weight: bold; color: white; text-align: center; }
-    .subtitle { font-size: 24px !important; font-weight: bold; color: #ffcc00; }
+    .title { font-size: 36px; font-weight: bold; color: white; text-align: center; }
+    .subtitle { font-size: 24px; font-weight: bold; color: #ffcc00; }
     .stSelectbox label, .stNumberInput label, .stSlider label { font-size: 18px; font-weight: bold; color: white; }
     .stButton>button { background-color: #ffcc00; color: #003366; font-size: 18px; font-weight: bold; border-radius: 10px; }
     .stButton>button:hover { background-color: #ff9900; color: white; }
@@ -36,19 +31,45 @@ st.markdown(
 st.markdown('<h1 class="title">Wine Quality Prediction 🍷🔬</h1>', unsafe_allow_html=True)
 st.write("Predicting Wine Quality based on wine parameters.")
 
-# Model Selection
-st.markdown('<h2 class="subtitle">Select Prediction Model 🔍</h2>', unsafe_allow_html=True)
-selected_model = st.selectbox("Select Prediction Model", list(MODEL_FILES.keys()))
-
-# Load Model
-model_path = MODEL_FILES[selected_model]
-if os.path.exists(model_path):
-    with open(model_path, "rb") as f:
+# Check if Model Exists
+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.")
+    st.error(f"❌ Model file '{MODEL_PATH}' not found! Please train and save the model first.")
+    
+    # Instructions to Save the Model
+    st.write("📌 **To save the model, run this in your Python script:**")
+    st.code(f"""
+import pickle
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import load_wine
+
+# Load sample dataset
+data = load_wine()
+X, y = data.data, data.target
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Create pipeline
+model = Pipeline([
+    ("scaler", StandardScaler()), 
+    ("classifier", RandomForestClassifier(n_estimators=100, random_state=42))
+])
+
+# Train model
+model.fit(X_train, y_train)
+
+# Save the pipeline
+with open("{MODEL_PATH}", "wb") as f:
+    pickle.dump(model, f)
+
+print("✅ Model saved successfully!")
+    """, language="python")
 
 # User Inputs
 fixed_acidity = st.number_input("Fixed Acidity", min_value=4.6, max_value=15.9, value=7.6)
@@ -63,7 +84,7 @@ pH = st.number_input("pH", min_value=2.74, max_value=4.01, value=3.12)
 sulphates = st.number_input("Sulphates", min_value=0.33, max_value=2.00, value=0.9)
 alcohol = st.number_input("Alcohol", min_value=8.4, max_value=14.9, value=10.2)
 
-# Prepare input for prediction
+# Prepare Input for Prediction
 input_data = np.array([[fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, 
                         free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol]])
 
@@ -73,4 +94,6 @@ if st.button("Predict Wine Quality"):
         prediction = model.predict(input_data)
         st.markdown(f'<p class="prediction">Predicted Wine Quality: {prediction[0]}</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 train the model and try again.")
+
+