Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,61 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import joblib
+import pandas as pd
+import numpy as np
 
-"""
-# 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])),
-    ))
+MODEL_PATH = 'src/vgsales_prediction.joblib' 
+FEATURES = ["Rank", "NA_Sales", "EU_Sales", "JP_Sales", "Other_Sales"]
+
+@st.cache_resource
+def load_linear_model():
+    try:
+        model = joblib.load(MODEL_PATH)
+        return model
+    except Exception as e:
+        st.error(f"Error loading the Linear Regression model. Ensure '{MODEL_PATH}' is uploaded. Error: {e}")
+        return None
+
+def predict_sales(model, input_data):
+    input_df = pd.DataFrame([input_data])[FEATURES]
+    
+    prediction = model.predict(input_df)
+    
+    return float(prediction[0])
+
+# --- Streamlit Interface ---
+st.set_page_config(page_title="Game Sales Predictor", layout="centered")
+st.title("🎮 Global Video Game Sales Prediction")
+st.markdown("Enter regional sales figures (in Millions) and Rank to predict total Global Sales.")
+
+model = load_linear_model()
+
+if model is not None:
+    st.sidebar.header("Sales Input (in Millions)")
+
+    rank = st.sidebar.number_input("Game Rank:", min_value=1, value=1000)
+    
+    na_sales = st.sidebar.number_input("North America Sales (NA_Sales):", min_value=0.0, value=0.5, step=0.01)
+    eu_sales = st.sidebar.number_input("Europe Sales (EU_Sales):", min_value=0.0, value=0.25, step=0.01)
+    jp_sales = st.sidebar.number_input("Japan Sales (JP_Sales):", min_value=0.0, value=0.1, step=0.01)
+    other_sales = st.sidebar.number_input("Other Sales (Other_Sales):", min_value=0.0, value=0.05, step=0.01)
+
+    input_data = {
+        "Rank": rank,
+        "NA_Sales": na_sales,
+        "EU_Sales": eu_sales,
+        "JP_Sales": jp_sales,
+        "Other_Sales": other_sales
+    }
+
+    st.subheader("Regional Sales Input (Millions):")
+    st.dataframe(pd.DataFrame([input_data]), hide_index=True)
+
+    if st.button("Predict Global Sales"):
+        with st.spinner('Calculating prediction...'):
+            predicted_global_sales = predict_sales(model, input_data)
+            
+            st.success("Prediction Successful!")
+            st.markdown("### Predicted Global Sales:")
+            st.markdown(f"**{predicted_global_sales:,.2f} Million**")
+            
+            st.info("Note: This simple linear model uses Rank and regional sales to predict the total.")