Update app.py

app.py

@@ -13,7 +13,7 @@ st.set_page_config(
 )
 
 # --- Title & Description ---
-st.title("📈Climate Forecasting")
+st.title("📈 ForecastMaster: Delhi Climate Forecasting")
 st.markdown(
     """
     Upload your historical weather CSV (with columns: `date`, `meantemp`, `humidity`, 
@@ -23,20 +23,28 @@ st.markdown(
 
 # --- Sidebar Controls ---
 st.sidebar.header("Application Features")
-features = {
+FEATURES = {
     "Temperature Forecast": "meantemp",
     "Humidity Forecast": "humidity",
     "Wind Speed Forecast": "wind_speed",
     "Mean Pressure Forecast": "meanpressure"
 }
-selected = {
-    label: st.sidebar.checkbox(label, value=True)
-    for label in features
-}
+# Checkbox for each feature
+selected = {label: st.sidebar.checkbox(label, value=True) for label in FEATURES}
+
+# Forecast horizon
 periods = st.sidebar.slider(
     "Days to Forecast", min_value=30, max_value=365, value=180, step=30
 )
 
+# --- Color Palette ---
+FEATURE_COLORS = {
+    "Temperature Forecast": "#EF553B",   # red
+    "Humidity Forecast": "#00CC96",      # green
+    "Wind Speed Forecast": "#636EFA",    # blue
+    "Mean Pressure Forecast": "#AB63FA", # purple
+}
+
 # --- File Uploader ---
 uploaded_file = st.file_uploader("Upload CSV", type=["csv"])
 if not uploaded_file:
@@ -45,37 +53,40 @@ if not uploaded_file:
 
 # --- Load & Preprocess ---
 df = pd.read_csv(uploaded_file)
-df.rename(columns={"date": "ds", "meantemp": "meantemp"}, inplace=True)
+df.rename(columns={"date": "ds"}, inplace=True)
 df["ds"] = pd.to_datetime(df["ds"])
 st.subheader("Data Preview")
 st.dataframe(df.head())
 
 # --- Forecasting Function ---
 def make_and_plot(df: pd.DataFrame, col: str, label: str):
-    # prepare
     tmp = df[["ds", col]].dropna().rename(columns={col: "y"})
     if tmp.empty:
         st.warning(f"No data for {label}. Skipping.")
         return
 
-    # fit
+    # Fit Prophet model
     m = Prophet()
     m.fit(tmp)
 
-    # predict
+    # Create future and forecast
     future = m.make_future_dataframe(periods=periods)
     fcst = m.predict(future)
 
-    # plot forecast
+    # Plot with custom color
+    color = FEATURE_COLORS.get(label, "#333333")
     st.subheader(f"{label}")
     fig = px.line(
-        fcst, x="ds", y="yhat",
+        fcst,
+        x="ds",
+        y="yhat",
         title=f"{label} Forecast",
-        labels={"ds": "Date", "yhat": f"Forecasted {label}"}
+        labels={"ds": "Date", "yhat": f"Forecasted {label}"},
+        color_discrete_sequence=[color]
     )
     st.plotly_chart(fig, use_container_width=True)
 
-    # show tail table
+    # Show latest forecast table
     st.write("Latest Forecast Values")
     st.dataframe(
         fcst[["ds", "yhat", "yhat_lower", "yhat_upper"]].tail(),
@@ -87,13 +98,13 @@ def make_and_plot(df: pd.DataFrame, col: str, label: str):
         }
     )
 
-    # components
+    # Trend & seasonality
     st.write("Trend & Seasonality")
     comp_fig = m.plot_components(fcst)
     st.pyplot(comp_fig)
 
-# --- Loop Through Selected Features ---
-for label, col in features.items():
+# --- Generate Forecasts ---
+for label, col in FEATURES.items():
     if selected[label]:
         make_and_plot(df, col, label)