Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,138 @@
-import altair as alt
 import numpy as np
 import pandas as pd
 import streamlit as st
+import joblib
+from pathlib import Path
 
-"""
-# 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])),
-    ))
+# -------------------------------
+# Page config
+# -------------------------------
+st.set_page_config(
+    page_title='Rainfall Probability Predictor (LogReg)',
+    page_icon='🌧️',
+    layout='centered'
+)
+
+st.title('🌧️ Rainfall Probability Predictor')
+st.write('Predicts the probability of rainfall (0–1) using a Logistic Regression model trained on weather features.')
+
+BASE_DIR = Path(__file__).resolve().parent
+MODEL_PATH = BASE_DIR / 'logistic_regression_model.pkl'
+FEATURE_NAMES_PATH = BASE_DIR / 'feature_names.pkl'
+
+
+@st.cache_resource
+def load_artifacts():
+    if not MODEL_PATH.exists():
+        raise FileNotFoundError(
+            f'Model not found: {MODEL_PATH.name}. Put it in the repo root (same folder as app.py).'
+        )
+    if not FEATURE_NAMES_PATH.exists():
+        raise FileNotFoundError(
+            f'Feature names not found: {FEATURE_NAMES_PATH.name}. Put it in the repo root (same folder as app.py).'
+        )
+
+    model = joblib.load(MODEL_PATH)
+    feature_names = joblib.load(FEATURE_NAMES_PATH)
+
+    if not isinstance(feature_names, list) or len(feature_names) == 0:
+        raise ValueError('feature_names.pkl must contain a non-empty list of column names.')
+
+    return model, feature_names
+
+
+def add_features(df: pd.DataFrame) -> pd.DataFrame:
+    df = df.copy()
+
+    # Temperature variability
+    df['temp_range'] = df['maxtemp'] - df['mintemp']
+    # Air saturation level
+    df['humidity_gap'] = df['humidity'] - df['dewpoint']
+    # Sunshine vs clouds
+    df['sunshine_ratio'] = df['sunshine'] / (df['cloud'] + 1)
+    # Wind intensity (simple interaction)
+    df['wind_energy'] = df['windspeed'] * df['winddirection']
+    # Seasonal pattern
+    df['sin_day'] = np.sin(2 * np.pi * df['day'] / 365)
+    df['cos_day'] = np.cos(2 * np.pi * df['day'] / 365)
+
+    return df
+
+
+model, feature_names = load_artifacts()
+
+st.subheader('Input features')
+
+# Note: ranges are generic. If you want, you can set them based on df1.describe().
+col1, col2 = st.columns(2)
+
+with col1:
+    day = st.number_input('day (1–365)', min_value=1, max_value=365, value=100, step=1)
+    pressure = st.number_input('pressure', value=1013.0, step=0.1)
+    maxtemp = st.number_input('maxtemp', value=20.0, step=0.1)
+    temperature = st.number_input('temperature', value=15.0, step=0.1)
+    mintemp = st.number_input('mintemp', value=10.0, step=0.1)
+
+with col2:
+    dewpoint = st.number_input('dewpoint', value=8.0, step=0.1)
+    humidity = st.number_input('humidity', value=70.0, step=0.1)
+    cloud = st.number_input('cloud', value=50.0, step=1.0)
+    sunshine = st.number_input('sunshine', value=5.0, step=0.1)
+    windspeed = st.number_input('windspeed', value=10.0, step=0.1)
+    winddirection = st.number_input('winddirection', value=180.0, step=1.0)
+
+# Build one-row dataframe with the ORIGINAL base features
+input_df = pd.DataFrame([{
+    'day': float(day),
+    'pressure': float(pressure),
+    'maxtemp': float(maxtemp),
+    'temperature': float(temperature),
+    'mintemp': float(mintemp),
+    'dewpoint': float(dewpoint),
+    'humidity': float(humidity),
+    'cloud': float(cloud),
+    'sunshine': float(sunshine),
+    'windspeed': float(windspeed),
+    'winddirection': float(winddirection)
+}])
+
+
+# Add engineered features (must match training)
+input_df = add_features(input_df)
+
+# Ensure correct feature order and missing columns safety
+missing_cols = [c for c in feature_names if c not in input_df.columns]
+extra_cols = [c for c in input_df.columns if c not in feature_names]
+
+if missing_cols:
+    st.error(f'Missing required feature columns: {missing_cols}')
+    st.stop()
+
+# Keep only the expected columns in the correct order
+X = input_df[feature_names].copy()
+
+st.divider()
+
+if st.button('Predict rainfall probability'):
+    try:
+        proba = float(model.predict_proba(X)[:, 1][0])
+        st.metric('Rainfall probability', f'{proba:.3f}', delta=None)
+        st.progress(min(max(proba, 0.0), 1.0))
+
+        if proba >= 0.7:
+            st.success('High chance of rainfall.')
+        elif proba >= 0.4:
+            st.warning('Medium chance of rainfall.')
+        else:
+            st.info('Low chance of rainfall.')
+
+        with st.expander('Show model input (debug)'):
+            st.write('Used feature columns (ordered):')
+            st.write(feature_names)
+            st.dataframe(X)
+
+        if extra_cols:
+            st.caption(f'Note: These columns were ignored (not in feature_names): {extra_cols}')
+
+    except Exception as e:
+        st.error(f'Prediction failed: {e}')