Update app.py

app.py

@@ -1,2 +1,76 @@
-import streamlit as st 
-st.write("Shweta_singh")
+import streamlit as st
+import pickle
+import pandas as pd
+import numpy as np
+import os
+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",
+}
+
+# Streamlit UI Setup
+st.set_page_config(page_title="Wine Quality Prediction 🍷🔬", layout="centered")
+
+# Custom 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; }
+    .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; }
+    .prediction { font-size: 26px; font-weight: bold; color: #32CD32; text-align: center; }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+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:
+        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
+fixed_acidity = st.number_input("Fixed Acidity", min_value=4.6, max_value=15.9, value=7.6)
+volatile_acidity = st.number_input("Volatile Acidity", min_value=0.12, max_value=1.58, value=1.2)
+citric_acid = st.number_input("Citric Acid", min_value=0.0, max_value=1.66, value=0.5)
+residual_sugar = st.number_input("Residual Sugar", min_value=0.9, max_value=15.5, value=5.8)
+chlorides = st.number_input("Chlorides", min_value=0.012, max_value=0.611, value=0.044)
+free_sulfur_dioxide = st.slider("Free Sulfur Dioxide", 1, 72, 15)
+total_sulfur_dioxide = st.slider("Total Sulfur Dioxide", 6, 289, 100)
+density = st.number_input("Density", min_value=0.99007, max_value=1.00369, value=1.00111)
+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
+input_data = np.array([[fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, 
+                        free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol]])
+
+# Prediction
+if st.button("Predict Wine Quality"):
+    if model_loaded:
+        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.")