Update app.py

app.py

@@ -44,13 +44,13 @@ with st.form("weather_form"):
     col1, col2 = st.columns(2)
 
     with col1:
-        temp = st.slider("🌡 Temperature (°C)", min_value=0.0, max_value=60.0, value=30.0)
-        humidity = st.slider("💧 Humidity (%)", min_value=0, max_value=100, value=60)
-        pressure = st.slider("🔵 Air Pressure (kPa)", min_value=0.0, max_value=5000.0, value=1000.0)  # Custom scale
+        temp = st.number_input("🌡 Temperature (°C)", min_value=0.0, max_value=60.0, value=30.0, step=0.1)
+        humidity = st.number_input("💧 Humidity (%)", min_value=0.0, max_value=100.0, value=60.0, step=0.1)
+        pressure = st.number_input("🔵 Air Pressure (kPa)", min_value=0.0, max_value=5000.0, value=1000.0, step=0.1)
 
     with col2:
-        wind_speed = st.slider("🌬 Wind Speed (m/s)", min_value=0.0, max_value=100.0, value=5.0)
-        wind_dir = st.slider("🧭 Wind Direction (°)", min_value=0, max_value=360, value=180)
+        wind_speed = st.number_input("🌬 Wind Speed (m/s)", min_value=0.0, max_value=100.0, value=5.0, step=0.1)
+        wind_dir = st.number_input("🧭 Wind Direction (°)", min_value=0, max_value=360, value=180, step=1)
         entry_date = st.date_input("📆 Entry Date", value=date.today())
 
     rain_label = st.selectbox("🌦 Did it rain?", ["No", "Yes"])
@@ -85,9 +85,8 @@ if submitted:
 
     # Define logic to check the rainfall
     def warning_label(rf_prob, cat_prob):
-        # If average predicted probability is greater than 1 mm (we consider the threshold to be >0.5 probability for rain)
         avg_prob = (rf_prob + cat_prob) / 2
-        if avg_prob > 0.5:  # Greater than 1mm equivalent
+        if avg_prob > 0.5:
             return "🌧 Rain"
         else:
             return "☀️ No Rain"
@@ -100,18 +99,26 @@ if submitted:
     with col2:
         st.metric("CatBoost", "🌧 Rain" if cat_pred else "☀️ No Rain", f"{cat_prob:.2f} probability")
 
-    # Calculate and display early warning level
     st.success(f"📢 Early Warning Level: **{warning_label(rf_prob, cat_prob)}**")
 
-    # Check for duplicates and add new entry to the dataset if needed
-    if str(next_day) in df["Date"].values:
+    # Append to dataset in memory (only during session)
+    if 'dataset' not in st.session_state:
+        st.session_state['dataset'] = pd.read_csv(DATA_PATH)
+
+    # Check for duplicates
+    if str(next_day) in st.session_state['dataset']["Date"].values:
         st.warning("⚠️ An entry for this date already exists. Skipping dataset update.")
     else:
-        df_new = pd.read_csv(DATA_PATH)
-        df_new = pd.concat([df_new, pd.DataFrame([input_row])], ignore_index=True)
-        df_new.to_csv(DATA_PATH, index=False)
-        st.success("✅ Data added to dataset.")
-
+        st.session_state['dataset'] = pd.concat(
+            [st.session_state['dataset'], pd.DataFrame([input_row])],
+            ignore_index=True
+        )
+        # Save updated file (only if running locally)
+        try:
+            st.session_state['dataset'].to_csv(DATA_PATH, index=False)
+            st.success("✅ Data added to dataset and saved locally.")
+        except:
+            st.warning("⚠️ Cannot save to file system on this platform. Data saved in session only.")
 
 # --- Evaluation Report ---
 with st.expander("📊 Model Evaluation"):