Update app.py

app.py

@@ -1,97 +1,138 @@
 import gradio as gr
 import numpy as np
 import plotly.graph_objects as go
-import plotly.express as px
-import pandas as pd
-from plotly.subplots import make_subplots
 
-# Manual TVM functions (no external finance libs)
-def npv(rate, cashflows):
-    total = 0.0
-    for i, cf in enumerate(cashflows):
-        total += cf / (1 + rate) ** i
-    return total
-
-def irr(cashflows, iterations=100):
-    rate = 0.1
-    for _ in range(iterations):
-        npv_val = npv(rate, cashflows)
-        npv_deriv = sum(-i * cf / (1 + rate) ** (i + 1) for i, cf in enumerate(cashflows) if cf != 0)
-        if abs(npv_deriv) < 1e-8:
-            break
-        rate -= npv_val / npv_deriv
-    return max(0, rate)
+def calculate_chemeng_tvm(capex, revenue, opex_fixed, opex_var, plant_life, marr_pct, salvage_pct, inflation_pct):
+    capex = float(capex)
+    revenue = float(revenue)
+    opex_fixed = float(opex_fixed)
+    opex_var = float(opex_var)
+    plant_life = int(plant_life)
+    marr = float(marr_pct) / 100
+    salvage = float(salvage_pct) / 100
+    inflation = float(inflation_pct) / 100
+    
+    # Generate cash flows
+    cashflows = [-capex]
+    for year in range(1, plant_life + 1):
+        opex_total = (opex_fixed + opex_var) * (1 + inflation) ** year
+        rev = revenue * (1 + inflation) ** year
+        net_cf = rev - opex_total
+        cashflows.append(net_cf)
+    cashflows[-1] += capex * salvage
+    
+    # NPV Calculation
+    npv_val = sum(cf / (1 + marr)**i for i, cf in enumerate(cashflows))
+    
+    # IRR Approximation (Newton-Raphson)
+    irr_guess = 0.1
+    for _ in range(50):
+        npv_est = sum(cf / (1 + irr_guess)**i for i, cf in enumerate(cashflows))
+        deriv = sum(-i * cf / (1 + irr_guess)**(i+1) for i, cf in enumerate(cashflows))
+        if abs(deriv) < 1e-10: break
+        irr_guess -= npv_est / deriv
+    irr_val = max(0, irr_guess * 100)
+    
+    # Payback Period
+    cum_cf = [0]
+    for cf in cashflows[1:]:
+        cum_cf.append(cum_cf[-1] + cf)
+    payback = next((i for i, total in enumerate(cum_cf) if total >= 0), plant_life)
+    
+    # Results HTML
+    results_html = f"""
+    <div style='font-family: Arial; padding: 20px;'>
+        <h2 style='color: #1f77b4;'>🧪 Chemical Engineering Economics</h2>
+        <table style='width: 100%; border-collapse: collapse; margin: 20px 0;'>
+            <tr style='background: #f0f8ff;'>
+                <th style='border: 1px solid #ddd; padding: 12px;'>Metric</th>
+                <th style='border: 1px solid #ddd; padding: 12px; text-align: right;'>Value</th>
+            </tr>
+            <tr>
+                <td style='border: 1px solid #ddd; padding: 12px;'><b>💎 NPV</b></td>
+                <td style='border: 1px solid #ddd; padding: 12px; text-align: right; font-size: 18px; color: {'green' if npv_val > 0 else 'red'};'>${npv_val:,.0f}</td>
+            </tr>
+            <tr style='background: #f9f9f9;'>
+                <td style='border: 1px solid #ddd; padding: 12px;'><b>⚡ IRR</b></td>
+                <td style='border: 1px solid #ddd; padding: 12px; text-align: right; font-size: 18px;'>{irr_val:.1f}%</td>
+            </tr>
+            <tr>
+                <td style='border: 1px solid #ddd; padding: 12px;'><b>⏱️ Payback</b></td>
+                <td style='border: 1px solid #ddd; padding: 12px; text-align: right;'>{payback:.1f} years</td>
+            </tr>
+            <tr style='background: #f9f9f9;'>
+                <td style='border: 1px solid #ddd; padding: 12px;'><b>📊 Profitability Index</b></td>
+                <td style='border: 1px solid #ddd; padding: 12px; text-align: right;'>{abs(npv_val/capex)*100:.1f}%</td>
+            </tr>
+        </table>
+        <div style='background: {'#d4edda' if npv_val > 0 else '#f8d7da'}; padding: 15px; border-radius: 8px; border-left: 5px solid {'#28a745' if npv_val > 0 else '#dc3545'};'>
+            <b>Status: {'✅ PROJECT VIABLE (NPV > 0)' if npv_val > 0 else '⚠️ REVIEW REQUIRED'}</b>
+        </div>
+    </div>
+    """
+    
+    # Cash Flow Chart
+    years = list(range(len(cashflows)))
+    fig = go.Figure()
+    fig.add_trace(go.Bar(x=years[1:], y=cashflows[1:], 
+                        name="Annual Cash Flow", marker_color='#1f77b4'))
+    fig.add_trace(go.Scatter(x=years, y=cum_cf, 
+                            mode='lines+markers', name="Cumulative CF",
+                            line=dict(color='#ff4444', width=4)))
+    fig.add_hline(y=0, line_dash="dash", line_color="gray", annotation_text="Breakeven")
+    fig.update_layout(
+        title="💸 Chemical Plant Cash Flow Analysis",
+        xaxis_title="Year", yaxis_title="Cash Flow ($)",
+        height=400, showlegend=True,
+        font=dict(size=12)
+    )
+    
+    return results_html, fig.to_html(full_html=False, div_id="cashflow-chart")
 
-class ChemEngTVM:
-    def __init__(self):
-        self.inflation = 0.03
+# Gradio Interface
+with gr.Blocks(title="ChemEng TVM Calculator") as demo:
+    gr.Markdown("# 🧪 Chemical Engineering TVM Calculator")
+    gr.Markdown("**CAPEX • OPEX • NPV • IRR • Plant Investment Analysis**")
     
-    def calculate(self, capex, opex_fixed, opex_var, revenue, plant_life, marr, salvage_pct, inflation_pct):
-        capex = float(capex)
-        opex_fixed, opex_var, revenue = map(float, [opex_fixed, opex_var, revenue])
-        plant_life, marr, salvage_pct, inflation_pct = map(float, [plant_life, marr, salvage_pct, inflation_pct])
-        marr /= 100
-        salvage_pct /= 100
-        inflation_pct /= 100
-        
-        # Generate cash flows
-        cashflows = [-capex]
-        for year in range(1, int(plant_life) + 1):
-            opex_total = (opex_fixed + opex_var) * (1 + inflation_pct) ** year
-            rev = revenue * (1 + inflation_pct) ** year
-            net_cf = rev - opex_total
-            cashflows.append(net_cf)
-        cashflows[-1] += capex * salvage_pct
-        
-        # Calculate metrics
-        npv_val = npv(marr, cashflows)
-        irr_val = irr(cashflows) * 100
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 📊 Chemical Plant Inputs")
+            
+            gr.Markdown("**💰 Investment & Revenue**")
+            with gr.Row():
+                capex_input = gr.Number(value=10000000, label="CAPEX ($)", precision=0)
+                revenue_input = gr.Number(value=5000000, label="Revenue/Year ($)", precision=0)
+            
+            gr.Markdown("**🏭 Operating Costs**")
+            with gr.Row():
+                opex_fixed_input = gr.Number(value=1500000, label="Fixed OPEX ($)", precision=0)
+                opex_var_input = gr.Number(value=2000000, label="Variable OPEX ($)", precision=0)
+            
+            gr.Markdown("**📅 Economic Parameters**")
+            with gr.Row():
+                plant_life_input = gr.Slider(5, 30, value=15, step=1, label="Plant Life (years)")
+                marr_input = gr.Slider(8, 20, value=12, step=1, label="MARR (%)")
+            
+            with gr.Row():
+                salvage_input = gr.Slider(0, 20, value=10, step=1, label="Salvage Value (%)")
+                inflation_input = gr.Slider(1, 8, value=3, step=0.5, label="Inflation (%)")
+            
+            calculate_btn = gr.Button("🔬 RUN ECONOMIC ANALYSIS", variant="primary", size="lg")
         
-        # Payback period
-        cum_cf = np.cumsum(cashflows)
-        payback = next((i for i, x in enumerate(cum_cf) if x >= 0), plant_life)
-        
-        # Create visualizations
-        years = np.arange(len(cashflows))
-        
-        # Cash flow chart
-        fig_cf = go.Figure()
-        fig_cf.add_trace(go.Bar(x=years[1:], y=cashflows[1:], 
-                               name="Annual Cash Flow", marker_color='#1f77b4'))
-        fig_cf.add_trace(go.Scatter(x=years, y=cum_cf, 
-                                   mode='lines+markers', name="Cumulative CF",
-                                   line=dict(color='red', width=4)))
-        fig_cf.add_hline(y=0, line_dash="dash", line_color="gray")
-        fig_cf.update_layout(
-            title="🧪 Chemical Plant Cash Flow Analysis",
-            xaxis_title="Year", yaxis_title="Cash Flow ($)",
-            height=400, showlegend=True
-        )
-        
-        # Sensitivity analysis
-        rev_range = np.linspace(revenue*0.6, revenue*1.4, 15)
-        npv_range = []
-        for r in rev_range:
-            temp_cf = [-capex]
-            for y in range(1, int(plant_life)+1):
-                opex_t = (opex_fixed + opex_var) * (1 + inflation_pct)**y
-                rev_t = r * (1 + inflation_pct)**y
-                temp_cf.append(rev_t - opex_t)
-            temp_cf[-1] += capex * salvage_pct
-            npv_range.append(npv(marr, temp_cf))
-        
-        fig_sens = px.line(x=rev_range/1e6, y=np.array(npv_range)/1e6,
-                          labels={'x':'Revenue ($M)', 'y':'NPV ($M)'},
-                          title="📈 NPV Sensitivity to Revenue")
-        fig_sens.add_hline(y=0, line_dash="dash", line_color="red")
-        
-        # Results table
-        df = pd.DataFrame({
-            'Year': years,
-            'Cash Flow': np.round(cashflows, 0),
-            'Cumulative': np.round(cum_cf, 0)
-        })
-        
-        # Format results
-        results = f"""
-## 🎯 **Economic Summary**
+        with gr.Column(scale=2):
+            results_output = gr.HTML()
+            chart_output = gr.HTML()
+    
+    # Connect button to function
+    calculate_btn.click(
+        calculate_chemeng_tvm,
+        inputs=[capex_input, revenue_input, opex_fixed_input, opex_var_input, 
+                plant_life_input, marr_input, salvage_input, inflation_input],
+        outputs=[results_output, chart_output]
+    )
+    
+    gr.Markdown("---")
+    gr.Markdown("*Professional tool for Chemical Engineers | Powered by Gradio*")
+
+# Launch (Hugging Face auto-runs this)
+demo.launch()