Update app.py

app.py

@@ -1,90 +1,77 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-from datetime import datetime, timedelta
-import random
 
-# App Configuration
+# Streamlit App Configuration
 st.set_page_config(page_title="Maintenance Prediction & Scheduling", layout="wide")
 
-# Sidebar for Navigation
+# Sidebar for Navigation and File Upload
 st.sidebar.title("Navigation")
-view_option = st.sidebar.radio("Choose View:", ["Home", "Upload Data", "View Predictions", "Schedule Maintenance"])
+view_option = st.sidebar.radio("Choose View:", ["Home", "Upload Data", "Maintenance Schedule", "Equipment Insights"])
+
+st.sidebar.title("Upload Data")
+data_upload = st.sidebar.file_uploader("Upload Equipment Data (CSV):", type=["csv"])
 
 # App Title
 st.title("Maintenance Prediction & Scheduling App")
-st.markdown("### Reduce downtime by predicting maintenance needs effectively.")
+st.markdown(
+    "This app predicts maintenance needs and schedules activities to minimize downtime for construction equipment like HVAC, elevators, and generators."
+)
 
-# Helper Functions
-def predict_maintenance(data):
-    """Simulate maintenance prediction based on input data."""
-    data["Predicted_Issue"] = np.where(data["Usage_Hours"] > 500, "Yes", "No")
-    data["Predicted_Downtime"] = np.where(data["Predicted_Issue"] == "Yes", random.randint(1, 5), 0)
-    return data
+# Helper Function - Process Uploaded Data
+def process_uploaded_data(uploaded_file):
+    try:
+        data = pd.read_csv(uploaded_file)
+        st.success("Data uploaded successfully!")
+        st.dataframe(data)
+        return data
+    except Exception as e:
+        st.error(f"Error processing the uploaded file: {e}")
+        return None
 
-def schedule_maintenance(data):
-    """Generate maintenance schedule based on predictions."""
-    today = datetime.now()
-    data["Scheduled_Maintenance_Date"] = data.apply(
-        lambda row: today + timedelta(days=random.randint(1, 14)) if row["Predicted_Issue"] == "Yes" else "N/A",
-        axis=1
-    )
-    return data
+# Main Views
+uploaded_data = None
 
-# Home Section
 if view_option == "Home":
-    st.image("https://via.placeholder.com/800x400.png?text=Maintenance+Prediction+App", caption="Welcome Screen", use_column_width=True)
+    st.image("https://via.placeholder.com/800x400.png?text=Maintenance+Scheduling+App", caption="Welcome Screen")
     st.markdown(
-        "### Key Features:\n- Upload IoT sensor data for equipment monitoring.\n- Predict equipment breakdowns and plan preventive maintenance.\n- Generate centralized health reports."
+        """
+        ### Key Features:
+        - Upload site data for analysis.
+        - Predict maintenance needs based on historical data.
+        - Schedule maintenance tasks and download the schedule.
+        """
     )
 
-# Upload Data Section
 elif view_option == "Upload Data":
     st.subheader("Upload Equipment Data")
-    data_upload = st.file_uploader("Upload your equipment data (CSV format):", type="csv")
     if data_upload:
-        try:
-            data = pd.read_csv(data_upload)
-            st.write("Uploaded Data:")
-            st.dataframe(data)
-        except Exception as e:
-            st.error(f"Error loading data: {e}")
+        uploaded_data = process_uploaded_data(data_upload)
 
-# View Predictions Section
-elif view_option == "View Predictions":
-    st.subheader("Predicted Maintenance Needs")
-    if data_upload:
-        try:
-            data = pd.read_csv(data_upload)
-            predicted_data = predict_maintenance(data)
-            st.write("Predicted Maintenance Data:")
-            st.dataframe(predicted_data)
-        except Exception as e:
-            st.error(f"Error processing predictions: {e}")
+elif view_option == "Maintenance Schedule":
+    st.subheader("Maintenance Schedule")
+    if data_upload and uploaded_data is not None:
+        st.write("Generating maintenance schedule...")
+        # Simulate maintenance schedule
+        uploaded_data["Maintenance_Due"] = np.where(uploaded_data["Condition_Score"] < 75, "Yes", "No")
+        st.dataframe(uploaded_data[["Equipment_ID", "Equipment_Type", "Maintenance_Due"]])
+        st.download_button(
+            label="Download Schedule as CSV",
+            data=uploaded_data.to_csv(index=False).encode('utf-8'),
+            file_name="maintenance_schedule.csv",
+            mime="text/csv",
+        )
     else:
-        st.info("Please upload equipment data in the 'Upload Data' section.")
+        st.info("Please upload data in the 'Upload Data' section.")
 
-# Schedule Maintenance Section
-elif view_option == "Schedule Maintenance":
-    st.subheader("Maintenance Scheduling")
-    if data_upload:
-        try:
-            data = pd.read_csv(data_upload)
-            predicted_data = predict_maintenance(data)
-            scheduled_data = schedule_maintenance(predicted_data)
-            st.write("Scheduled Maintenance Plan:")
-            st.dataframe(scheduled_data)
-            st.download_button(
-                label="Download Maintenance Schedule",
-                data=scheduled_data.to_csv(index=False),
-                file_name="maintenance_schedule.csv",
-                mime="text/csv"
-            )
-        except Exception as e:
-            st.error(f"Error generating schedule: {e}")
+elif view_option == "Equipment Insights":
+    st.subheader("Equipment Insights")
+    if data_upload and uploaded_data is not None:
+        st.write("Analyzing equipment performance...")
+        st.bar_chart(uploaded_data[["Usage_Hours", "Condition_Score"]].set_index(uploaded_data["Equipment_Type"]))
     else:
-        st.info("Please upload equipment data in the 'Upload Data' section.")
+        st.info("Please upload data in the 'Upload Data' section.")
 
 # Footer
 st.markdown("---")
-st.markdown("Designed and developed by [Your Name] - Advanced Maintenance Solutions for the Construction Industry.")
+st.markdown("Developed by [Your Name] - Enhancing Maintenance Planning with AI")