Update app.py

app.py

@@ -0,0 +1,196 @@
+# app.py
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+# Set page title and layout
+st.set_page_config(page_title="Water System Analysis", layout="centered")
+st.title("Water Network Leak, Surge, and Gas Flow Analyzer")
+
+# Allow user to upload an Excel or CSV file
+uploaded_file = st.file_uploader("Upload Excel or CSV File", type=["xlsx", "csv"])
+
+if uploaded_file:
+    try:
+        # Read the uploaded file into a pandas DataFrame
+        if uploaded_file.name.endswith('.xlsx'):
+            df = pd.read_excel(uploaded_file)
+        else:
+            df = pd.read_csv(uploaded_file)
+        st.success("Data loaded successfully!")
+    except Exception as e:
+        st.error(f"Error reading the file: {e}")
+        st.stop()
+    
+    # Detect if a "Serial Number" column exists (case-insensitive)
+    serial_col = None
+    for col in df.columns:
+        if col.strip().lower().startswith("serial"):
+            serial_col = col
+            break
+
+    # If serial number column is found, let user select one
+    if serial_col:
+        serial_list = df[serial_col].unique().tolist()
+        selected_serial = st.selectbox(f"Select {serial_col}", serial_list)
+        df_filtered = df[df[serial_col] == selected_serial]
+        if df_filtered.empty:
+            st.warning("No data found for the selected serial number.")
+            st.stop()
+    else:
+        # If no serial number column, use the entire dataset
+        df_filtered = df
+        selected_serial = None
+
+    # Let the user select which parameter (column) to analyze
+    # Exclude the serial column itself from selection
+    columns = [col for col in df_filtered.columns if col != serial_col]
+    selected_column = st.selectbox("Select a parameter to analyze", columns)
+    
+    if selected_column:
+        # Retrieve the value(s) for the selected parameter
+        # If only one row is present, get the scalar; otherwise get list of values
+        if len(df_filtered) == 1:
+            value = df_filtered[selected_column].iloc[0]
+        else:
+            value = df_filtered[selected_column].tolist()
+        rec = ""  # This will hold the recommendation string
+        
+        # Leak detection (Water Pressure)
+        if "water pressure" in selected_column.lower():
+            # Compute mean pressure if multiple readings, else use the single value
+            if isinstance(value, list):
+                mean_val = np.mean(value)
+            else:
+                mean_val = value
+            # Apply simple threshold rules
+            if mean_val < 20:
+                rec = ("🚨 **Alert:** The water pressure is significantly low. "
+                       "This suggests a strong likelihood of a leak or supply issue. "
+                       "Inspect the pipeline and valves immediately. ")
+            elif mean_val < 40:
+                rec = ("⚠️ The water pressure is on the lower side. "
+                       "There may be a leak or high demand. "
+                       "Monitor the system closely and check for any anomalies. ")
+            elif mean_val > 80:
+                rec = ("⚠️ The water pressure is very high. "
+                       "Ensure the pressure-regulating devices are functioning. "
+                       "High pressure can stress the pipes. ")
+            else:
+                rec = ("✅ The water pressure is within normal range. "
+                       "No immediate issues detected for leaks based on pressure. ")
+        
+        # Surge detection (Transient Pressure)
+        elif "transient pressure" in selected_column.lower():
+            # Use the highest transient pressure to check for spikes
+            if isinstance(value, list):
+                peak_val = np.max(value)
+            else:
+                peak_val = value
+            if peak_val > 60:
+                rec = ("🚨 **Alert:** Detected a high transient pressure (pressure surge). "
+                       "This could indicate water hammer or sudden valve closure. "
+                       "Check surge protection devices and ensure valves operate smoothly. ")
+            else:
+                rec = ("✅ Transient pressure levels are within a safe range. "
+                       "No significant pressure surges detected. ")
+        
+        # Gas flow analysis
+        elif "gas flow" in selected_column.lower():
+            if isinstance(value, list):
+                mean_flow = np.mean(value)
+            else:
+                mean_flow = value
+            if mean_flow > 150:
+                rec = ("⚠️ Gas flow rate is high. "
+                       "This could indicate heavy usage or a potential leak. "
+                       "Check the gas lines and usage patterns. ")
+            elif mean_flow < 50:
+                rec = ("⚠️ Gas flow rate is very low. "
+                       "Ensure that valves are open or that the demand is correct. "
+                       "No gas might indicate a supply issue. ")
+            else:
+                rec = ("✅ Gas flow rate is within normal operating range. "
+                       "No obvious anomalies detected in gas flow. ")
+        
+        # Network Design Type advice
+        elif "network design" in selected_column.lower():
+            design = str(df_filtered[selected_column].iloc[0]).strip().lower()
+            if "grid" in design:
+                rec = ("✅ A grid network provides redundancy and reliability. "
+                       "Ensure maintenance of all loops for continuous supply. ")
+            elif "radial" in design:
+                rec = ("⚠️ A radial network is simpler but has less redundancy. "
+                       "A failure in a main line may isolate downstream areas. "
+                       "Consider backup routes if possible. ")
+            elif "ring" in design or "loop" in design:
+                rec = ("✅ A ring (loop) network allows for good circulation and redundancy. "
+                       "Keep valves balanced to maintain pressure. ")
+            else:
+                rec = ("✅ The network design type is noted. "
+                       "Regular inspection ensures reliability. ")
+        
+        # Population Density advice
+        elif "population density" in selected_column.lower():
+            val = df_filtered[selected_column].iloc[0]
+            if val > 1500:
+                rec = ("⚠️ High population density indicates heavy demand on the water network. "
+                       "Ensure the infrastructure is scaled properly to serve the community. "
+                       "Consider capacity upgrades or demand management programs. ")
+            elif val > 800:
+                rec = ("✅ Population density is moderate. "
+                       "Maintain regular monitoring to meet demand and plan for growth. ")
+            else:
+                rec = ("✅ Low population density suggests light demand. "
+                       "Focus on efficient resource allocation and cost-effective maintenance. ")
+        
+        # Average Water Usage advice
+        elif "water usage" in selected_column.lower():
+            val = df_filtered[selected_column].iloc[0]
+            if val > 200:
+                rec = ("⚠️ Very high per-capita water usage detected. "
+                       "Promote water conservation measures and check for leaks in consumption systems. ")
+            elif val > 120:
+                rec = ("✅ Average water usage is within a moderate range. "
+                       "Continue to encourage efficient use of water. ")
+            else:
+                rec = ("✅ Water usage per person is low. "
+                       "This is efficient, but ensure basic needs are met. ")
+        
+        # Available Water Resources advice
+        elif "available water" in selected_column.lower():
+            val = df_filtered[selected_column].iloc[0]
+            if val < 80:
+                rec = ("⚠️ Limited available water resources. "
+                       "Implement conservation strategies and consider alternative sources. ")
+            elif val > 150:
+                rec = ("✅ Ample water resources are available. "
+                       "Still practice sustainable usage to maintain supply levels. ")
+            else:
+                rec = ("✅ Water resource levels are moderate. "
+                       "Monitor usage rates to avoid shortages. ")
+        
+        # Pipe Sizes advice
+        elif "pipe sizes" in selected_column.lower():
+            val = df_filtered[selected_column].iloc[0]
+            if val < 30:
+                rec = ("⚠️ Small pipe diameter detected. "
+                       "This may limit flow capacity. "
+                       "Check for potential bottlenecks in the network. ")
+            elif val > 40:
+                rec = ("✅ Pipe size is large. "
+                       "Adequate flow capacity is available. "
+                       "Ensure regular inspections for longevity. ")
+            else:
+                rec = ("✅ Standard pipe size. "
+                       "Flow capacity is adequate for normal operation. ")
+        
+        else:
+            # Default case if the column is unrecognized
+            rec = ("✅ No specific recommendations for the selected parameter. "
+                   "Data value is noted and appears within expected range. ")
+        
+        # Display the recommendations
+        st.markdown(f"### Recommendations for **{selected_column}**")
+        st.write(rec)