Upload 4 files

app_hf.py

@@ -0,0 +1,117 @@
+import streamlit as st
+import pandas as pd
+import mlflow.sklearn
+import numpy as np
+import os
+
+from src.inference.predict import load_model_from_run, predict
+from src.data.data_loader import load_processed_data
+import joblib
+
+# --- 1. CONFIG & MODEL LOADING ---
+st.set_page_config(page_title="AQI Master Predictor", page_icon="🌬️", layout="wide")
+
+
+@st.cache_resource
+def load_final_model():
+    return joblib.load("model.pkl")
+
+try:
+    model = load_final_model()
+except Exception as e:
+    st.error(f"Error loading model from path. Please check if the path is correct. Details: {e}")
+    st.stop()
+
+# --- 2. AQI CATEGORY LOGIC ---
+def get_aqi_info(aqi):
+    if aqi <= 50:
+        return "Good", "🟢", "Air quality is considered satisfactory, and air pollution poses little or no risk."
+    elif aqi <= 100:
+        return "Satisfactory", "🟡", "Air quality is acceptable; however, there may be a moderate health concern for a very small number of people."
+    elif aqi <= 200:
+        return "Moderate", "🟠", "Members of sensitive groups may experience health effects. The general public is not likely to be affected."
+    elif aqi <= 300:
+        return "Poor", "🔴", "Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects."
+    elif aqi <= 400:
+        return "Very Poor", "🟣", "Health alert: everyone may experience more serious health effects."
+    else:
+        return "Severe", "🟤", "Health warnings of emergency conditions. The entire population is more likely to be affected."
+
+# --- 3. UI SIDEBAR (Inputs) ---
+st.sidebar.header("📍 Location & Time")
+
+@st.cache_data
+def get_city_list():
+    df = load_processed_data("data/cleaned/df_cleaned.csv")
+    return sorted(df["City"].dropna().unique())
+
+@st.cache_data
+def get_year_list():
+    df = load_processed_data("data/cleaned/df_cleaned.csv")
+    return sorted(df["Year"].dropna().unique())
+
+
+cities = get_city_list()
+years = get_year_list()
+
+city = st.sidebar.selectbox(
+    "City",
+    cities,
+    index=cities.index("Delhi") if "Delhi" in cities else 0
+)
+
+season = st.sidebar.selectbox("Season", ["Winter", "Summer", "Monsoon", "Post-Monsoon"])
+
+month = st.sidebar.slider("Month", 1, 12, 3)
+
+year = st.sidebar.selectbox(
+    "Year",
+    years,
+    index=len(years) - 1  # default to latest available year
+)
+
+st.sidebar.header("🧪 Pollutant Levels (µg/m³)")
+pm25 = st.sidebar.number_input("PM2.5", value=45.0)
+pm10 = st.sidebar.number_input("PM10", value=90.0)
+no2 = st.sidebar.number_input("NO2", value=20.0)
+no = st.sidebar.number_input("NO", value=10.0)
+nox = st.sidebar.number_input("NOx", value=30.0)
+nh3 = st.sidebar.number_input("NH3", value=5.0)
+co = st.sidebar.number_input("CO", value=0.5)
+so2 = st.sidebar.number_input("SO2", value=10.0)
+o3 = st.sidebar.number_input("O3", value=35.0)
+benzene = st.sidebar.number_input("Benzene", value=2.0)
+toluene = st.sidebar.number_input("Toluene", value=4.0)
+
+# --- 4. MAIN PAGE ---
+st.title("🌍 Real-time AQI Prediction System")
+st.markdown(f"Currently predicting for **{city}** in **{season} {year}**.")
+
+# Prediction Logic
+input_df = pd.DataFrame({
+    'City': [city], 'PM2.5': [pm25], 'PM10': [pm10], 'NO': [no], 
+    'NO2': [no2], 'NOx': [nox], 'NH3': [nh3], 'CO': [co], 
+    'SO2': [so2], 'O3': [o3], 'Benzene': [benzene], 
+    'Toluene': [toluene], 'Month': [month], 'Year': [year], 'Season': [season]
+})
+
+if st.button("Generate Prediction", use_container_width=True):
+    prediction = predict(model, input_df)[0]
+    label, icon, desc = get_aqi_info(prediction)
+    
+    st.divider()
+    
+    # Results Display
+    c1, c2 = st.columns([1, 2])
+    with c1:
+        st.metric(label="Calculated AQI Score", value=round(prediction, 2))
+    with c2:
+        st.subheader(f"{icon} Status: {label}")
+        st.write(desc)
+
+    # Health Recommendations (Fun Addition)
+    st.info("**Health Advice:** " + 
+            ("Wear a mask if going outside." if prediction > 150 else "Enjoy your day! Air quality is safe.")
+    )
+else:
+    st.write("Adjust the values in the sidebar and click the button to see results.")

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:79e1f09e2611132915e5e96f691339a6fa32a0c70355050ea1a54b30de95c018
+size 158722034