Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,101 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import joblib
+import numpy as np
+
+# Load Model
+@st.cache_resource
+def load_artifacts():
+    try:
+        return joblib.load('src/backpack_model.joblib')
+    except FileNotFoundError:
+        st.error("Model file 'backpack_model.joblib' not found. Please upload it to the Files section.")
+        return None
+
+artifacts = load_artifacts()
+
+if artifacts:
+    model = artifacts['model']
+    encoders = artifacts['encoders']
+    size_mapping = artifacts['size_mapping']
+    medians = artifacts.get('medians', {})
+
+# Page Configuration
+st.set_page_config(
+    page_title="Backpack Price Predictor",
+    page_icon="🎒",
+    layout="centered")
+
+st.title("🎒 Backpack Price Prediction AI")
+st.markdown("""
+    This AI tool predicts the price of a backpack based on its features.
+    \n**Adjust the values below and click 'Predict Price'.**
+""")
+
+# Input 
+if artifacts:
+    with st.form("prediction_form"):
+        st.subheader("1. Backpack Features")
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            brand = st.selectbox("Brand", options=encoders['Brand'].classes_)
+            material = st.selectbox("Material", options=encoders['Material'].classes_)
+            style = st.selectbox("Style", options=encoders['Style'].classes_)
+            color = st.selectbox("Color", options=encoders['Color'].classes_)
+            
+        with col2:
+            size = st.selectbox("Size", options=['Small', 'Medium', 'Large', 'Unknown'])
+            compartments = st.number_input("Number of Compartments", min_value=0, max_value=20, value=5, step=1)
+            weight_capacity = st.number_input("Weight Capacity (kg)", min_value=0.0, max_value=100.0, value=10.0, step=0.5)
+            
+        st.subheader("2. Extra Features")
+        col3, col4 = st.columns(2)
+        with col3:
+            laptop = st.selectbox("Laptop Compartment?", options=['Yes', 'No'])
+        with col4:
+            waterproof = st.selectbox("Waterproof?", options=['Yes', 'No'])
+
+        submit_btn = st.form_submit_button("Predict Price 🚀", type="primary")
+
+    # Prediction 
+    if submit_btn:
+        input_data = {
+            'Brand': brand,
+            'Material': material,
+            'Style': style,
+            'Color': color,
+            'Laptop Compartment': laptop,
+            'Waterproof': waterproof,
+            'Size': size,
+            'Compartments': compartments,
+            'Weight Capacity (kg)': weight_capacity}
+        
+        df_input = pd.DataFrame([input_data])
+        
+        try:
+            
+            df_input['Size_Encoded'] = df_input['Size'].map(size_mapping).fillna(-1)
+            
+            encode_cols = ['Brand', 'Material', 'Style', 'Color', 'Laptop Compartment', 'Waterproof']
+            
+            for col in encode_cols:
+                df_input[f'{col}_Encoded'] = encoders[col].transform([input_data[col]])
+            
+            model_features = model.get_booster().feature_names
+            
+            X_pred = df_input[model_features]
+            
+            prediction = model.predict(X_pred)[0]
+            
+            st.success(f"💰 Estimated Price: **${prediction:.2f}**")
+            
+            st.progress(min(int(prediction), 300) / 300)
+            
+        except Exception as e:
+            st.error(f"An error occurred during prediction: {e}")
+            st.warning("Technical Detail: Ensure the input categories match the training data.")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+else:
+    st.warning("Please upload the 'backpack_model.joblib' file to the app files.")