Update app.py

app.py

@@ -3,23 +3,28 @@ import numpy as np
 
 # Optimization function
 def optimize_process(resource_allocation, machine_efficiency, production_goal, time_frame, waste_tolerance):
-    # Simplified model: Replace with actual optimization logic (e.g., regression, linear programming)
-    
-    # Example output based on basic assumptions
-    machines_needed = np.ceil(production_goal / (time_frame * machine_efficiency))
-    expected_output = machines_needed * time_frame * machine_efficiency
+    # Calculate current production capacity
+    current_capacity = resource_allocation * machine_efficiency * time_frame
+    machines_needed = np.ceil(production_goal / (machine_efficiency * time_frame))
+    expected_output = min(current_capacity, production_goal)
     waste_output = (expected_output * waste_tolerance) / 100
     
-    # Calculate the efficiency improvement needed
-    target_efficiency = 1.0  # Target efficiency is 100%
-    efficiency_improvement_needed = max(0, (target_efficiency - machine_efficiency) * 100)
-    
+    # Determine realistic efficiency improvement recommendation
+    if current_capacity < production_goal:
+        required_efficiency = production_goal / (resource_allocation * time_frame)
+        realistic_efficiency = min(95, required_efficiency * 100)  # Cap realistic efficiency at 95%
+        efficiency_improvement_needed = max(0, realistic_efficiency - machine_efficiency * 100)
+    else:
+        realistic_efficiency = machine_efficiency * 100
+        efficiency_improvement_needed = 0
+
     return {
         'Machines Needed': machines_needed,
         'Expected Output': expected_output,
         'Waste Output': waste_output,
         'Efficiency Improvement Needed': efficiency_improvement_needed,
-        'Optimization Recommendation': f"Machines efficiency should be at least {target_efficiency * 100}% for the best results."
+        'Recommendation Efficiency': realistic_efficiency,
+        'Optimization Recommendation': f"Machines efficiency should be at least {realistic_efficiency:.1f}% for the best results."
     }
 
 # Streamlit App Layout
@@ -33,7 +38,7 @@ This tool helps you **optimize your manufacturing processes** by adjusting **res
 with st.sidebar:
     st.header("🔧 Enter Manufacturing Parameters")
     resource_allocation = st.number_input("🔢 Number of machines available", min_value=1, max_value=100, value=10, step=1)
-    machine_efficiency = st.slider("⚙️ Machine Efficiency (%)", min_value=0, max_value=100, value=80)
+    machine_efficiency = st.slider("⚙️ Machine Efficiency (%)", min_value=50, max_value=95, value=80, step=1)  # Max efficiency capped at 95%
     production_goal = st.number_input("📈 Desired production goal (units)", min_value=1, max_value=1000, value=100, step=1)
     time_frame = st.number_input("⏳ Production time frame (hours)", min_value=1, max_value=24, value=8, step=1)
     waste_tolerance = st.slider("♻️ Maximum waste tolerance (%)", min_value=0, max_value=100, value=5)
@@ -43,14 +48,20 @@ st.subheader("🔍 Optimization Results")
 
 if st.button("🚀 Optimize Process"):
     # Get optimization results
-    optimized_output = optimize_process(resource_allocation, machine_efficiency / 100, production_goal, time_frame, waste_tolerance)
+    optimized_output = optimize_process(
+        resource_allocation, 
+        machine_efficiency / 100, 
+        production_goal, 
+        time_frame, 
+        waste_tolerance
+    )
     
     # Display the optimized configuration
     st.write(f"### 🛠️ Optimized Configuration:")
-    st.write(f"**Machines Needed**: {optimized_output['Machines Needed']}")
+    st.write(f"**Machines Needed**: {int(optimized_output['Machines Needed'])}")
     st.write(f"**Expected Output**: {optimized_output['Expected Output']} units")
-    st.write(f"**Expected Waste**: {optimized_output['Waste Output']} units")
-    st.write(f"**Efficiency Improvement Needed**: {optimized_output['Efficiency Improvement Needed']}%")
+    st.write(f"**Expected Waste**: {optimized_output['Waste Output']:.2f} units")
+    st.write(f"**Efficiency Improvement Needed**: {optimized_output['Efficiency Improvement Needed']:.2f}%")
     st.write(f"**Recommendation**: {optimized_output['Optimization Recommendation']}")
 
     # Generate Optimization Report
@@ -62,12 +73,12 @@ if st.button("🚀 Optimize Process"):
     - **Resource Allocation**: The number of machines available is adequate, but you could potentially increase the machine count to optimize the process.
     
     ### Suggestions for Improvement:
-    1. **Improve Machine Efficiency**: A **{optimized_output['Efficiency Improvement Needed']}%** increase in machine efficiency will help meet the desired production standards.
+    1. **Improve Machine Efficiency**: A **{optimized_output['Efficiency Improvement Needed']:.2f}%** increase in machine efficiency will help meet the desired production standards.
     2. **Increase Machines**: Allocating more machines could help meet the production goal faster and reduce the time required.
     3. **Reduce Downtime**: Consider adjusting shift lengths or optimizing machine usage to reduce downtime and improve efficiency.
     
     ### Next Steps:
-    - Aim to **improve machine efficiency by {optimized_output['Efficiency Improvement Needed']}%** for optimal results.
+    - Aim to **improve machine efficiency to {optimized_output['Recommendation Efficiency']:.1f}%** for optimal results.
     - **Monitor waste** closely to reduce it further and ensure the production process remains efficient.
     - Review your **production goals** to ensure you are using the most efficient configuration of resources.
     """)