Create home.py

home.py

@@ -0,0 +1,88 @@
+import streamlit as st
+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
+
+# Streamlit UI
+st.set_page_config(page_title="🍷 Wine Quality Predictor", layout="centered")
+
+# Custom Styling for Background and Text
+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;
+    }
+    .stSlider label, .stNumberInput label {
+        font-size: 20px !important;
+        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("Predict the quality of wine based on its properties.")
+
+# Check if model file 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.")
+
+# 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.write("*Powered by Machine Learning*")