Update app.py

app.py

@@ -2,18 +2,9 @@ import streamlit as st
 import pickle
 import numpy as np
 import os
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import StandardScaler
 
-# Define Model File Paths
-MODEL_FILES = {
-    "Random Forest": "random_forest_pipeline.pkl",  
-    "Decision Tree": "decision_tree_pipeline.pkl",
-    "KNN": "knn_pipeline.pkl",
-    "Bagging": "bagging_pipeline.pkl",
-    "Voting": "voting_pipeline.pkl",
-}
+# Define Model File Path
+MODEL_PATH = "random_forest_pipeline.pkl"
 
 # Streamlit UI Setup
 st.set_page_config(page_title="Wine Quality Prediction 🍷🔬", layout="centered")
@@ -37,42 +28,14 @@ st.markdown(
 st.markdown('<h1 class="title">Wine Quality Prediction 🍷🔬</h1>', unsafe_allow_html=True)
 st.write("Predicting Wine Quality based on wine parameters.")
 
-# Select Model
-st.markdown('<h2 class="subtitle">Select Prediction Model 🔍</h2>', unsafe_allow_html=True)
-selected_model = st.selectbox("Select Prediction Model", list(MODEL_FILES.keys()))
-
-# Check if Model Exists
-model_path = MODEL_FILES[selected_model]
-if os.path.exists(model_path):
-    with open(model_path, "rb") as f:
+# Load Model if Available
+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 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
-
-# Assuming X_train, y_train are defined
-model = Pipeline([
-    ("scaler", StandardScaler()), 
-    ("classifier", RandomForestClassifier(n_estimators=100, random_state=42))
-])
-
-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")
+    st.error(f"❌ Model file '{MODEL_PATH}' not found! Please upload the trained model.")
 
 # User Inputs
 fixed_acidity = st.number_input("Fixed Acidity", min_value=4.6, max_value=15.9, value=7.6)
@@ -91,13 +54,12 @@ alcohol = st.number_input("Alcohol", min_value=8.4, max_value=14.9, value=10.2)
 input_data = np.array([[fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, 
                         free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol]])
 
-# Prediction
+# Prediction Button
 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 train the model and try again.")
-
+        st.error(f"❌ Model file '{MODEL_PATH}' not found. Please train the model and try again.")