Update app.py

app.py

@@ -1,75 +1,233 @@
-import matplotlib
-matplotlib.use("Agg")  # IMPORTANT: prevents runtime exit on HF
-
-import streamlit as st
 import numpy as np
-import pandas as pd
 import matplotlib.pyplot as plt
-from scipy.optimize import minimize
+import gradio as gr
 
-# ---------------- PAGE CONFIG ----------------
-st.set_page_config(page_title="Shaking Table Digital Twin", layout="centered")
+# =============================================
+# Constants & Mineral Densities (for physical influence)
+# =============================================
+minerals = ['Gold', 'Ilmenite', 'Rutile', 'Monazite']
+densities = {'Gold': 19.3, 'Ilmenite': 4.7, 'Rutile': 4.2, 'Monazite': 5.2}
+max_density = 19.3  # Gold for normalization
 
-st.title("Digital Twin – Shaking Table")
-st.write("Attock Placer Plant | CEP Project")
+# Fixed upstream feed to shaking table (as before)
+feed_rate_default = 300.0  # tph - but we'll scale with user input
+feed_to_shaking = {
+    'Gold': 3.744e-06,      # enriched g/t fraction
+    'Ilmenite': 0.05625,
+    'Rutile': 0.0135,
+    'Monazite': 0.006,
+    'Gangue': 0.924  # approximate balance
+}
 
-# ---------------- DATA ----------------
-minerals = ["Gold", "Ilmenite", "Rutile", "Monazite"]
+# =============================================
+# Physical-inspired Digital Twin Model
+# =============================================
+def shaking_table_model(params, feed_grades, feed_rate_tph):
+    stroke_len = params['stroke_len']        # mm, 8-25
+    freq = params['freq']                    # strokes/min, 250-350
+    tilt = params['tilt']                    # degrees, 2-8
+    wash_water = params['wash_water']        # arbitrary units 5-20
+    # Other params ignored for simplicity but can be extended
 
-plant_data = {
-    "Gold": [0.75, 0.15, 0.10],
-    "Ilmenite": [0.60, 0.25, 0.15],
-    "Rutile": [0.65, 0.20, 0.15],
-    "Monazite": [0.70, 0.20, 0.10]
-}
+    # Base mass yields influenced by parameters (empirical)
+    # Higher tilt & wash water → lower conc yield (sharper separation)
+    base_yield_conc = 50 - 3*(tilt - 5) - 0.5*(wash_water - 12)
+    yield_conc = np.clip(base_yield_conc + 0.05*(300 - freq), 10, 45)
 
-# ---------------- MODEL ----------------
-def shaking_table(params):
-    results = {}
-    i = 0
-    for m in minerals:
-        Rc = params[i]
-        Rm = params[i + 1]
-        Rt = max(0.0, 1.0 - Rc - Rm)
-        results[m] = [Rc, Rm, Rt]
-        i += 2
-    return results
-
-def error_fn(params):
-    res = shaking_table(params)
-    err = 0.0
+    # Middlings ~ proportional
+    yield_midd = yield_conc * 1.2
+    yield_tail = 100 - yield_conc - yield_midd
+    if yield_tail < 0:
+        yield_tail = 10
+        yield_midd = 100 - yield_conc - yield_tail
+
+    model_rec = {}
+    for minrl in minerals:
+        rho_norm = densities[minrl] / max_density
+        # Higher density → higher recovery to conc
+        # Optimal around stroke 18mm, freq 300, tilt 5°, wash 12
+        stroke_effect = 100 * (1 - 0.5 * ((stroke_len - 18)/10)**2)
+        freq_effect = 100 * (freq / 350)
+        tilt_effect = 100 * (1 - 0.8 * ((tilt - 5)/3)**2)
+        wash_effect = 100 * (1 - 0.6 * ((wash_water - 12)/8)**2)
+
+        rec_conc = 50 + rho_norm * (stroke_effect + freq_effect + tilt_effect + wash_effect - 200)
+        rec_conc = np.clip(rec_conc, 30, 95)
+
+        rec_tail = 100 - rec_conc - 20  # midd approx
+        rec_midd = 100 - rec_conc - rec_tail
+        if rec_midd < 0:
+            rec_midd = 0
+            rec_tail = 100 - rec_conc
+
+        model_rec[minrl] = {'Conc': rec_conc, 'Midd': rec_midd, 'Tail': rec_tail}
+
+    # Compute grades & rates
+    y_conc_frac = yield_conc / 100.0
+    mass_rate_conc = feed_rate_tph * y_conc_frac
+
+    grades_conc = {}
+    rates_conc = {}
     for m in minerals:
-        err += np.sum((np.array(res[m]) - np.array(plant_data[m])) ** 2)
-    return err
+        rec = model_rec[m]['Conc'] / 100.0
+        mineral_feed = feed_rate_tph * feed_grades[m]
+        rate_conc = mineral_feed * rec
+        rates_conc[m] = rate_conc
+        if m == 'Gold':
+            grades_conc[m] = (rate_conc / mass_rate_conc) * 1e6 if mass_rate_conc > 0 else 0
+        else:
+            grades_conc[m] = (rate_conc / mass_rate_conc) * 100 if mass_rate_conc > 0 else 0
+
+    return model_rec, yield_conc, yield_midd, yield_tail, grades_conc, rates_conc
+
+# =============================================
+# Target Rates Mode (inverse: adjust params to hit user target rates)
+# =============================================
+def find_parameters_for_targets(target_rates_conc, feed_grades, feed_rate_tph):
+    # target_rates_conc dict mineral: tph to conc
+
+    def error(p):
+        params = {'stroke_len': p[0], 'freq': p[1], 'tilt': p[2], 'wash_water': p[3]}
+        _, _, _, _, _, rates = shaking_table_model(params, feed_grades, feed_rate_tph)
+        err = 0
+        for m in minerals:
+            err += (rates[m] - target_rates_conc[m]) ** 2
+        return err
+
+    from scipy.optimize import minimize
+    initial = [18, 300, 5, 12]
+    bounds = [(8,25), (250,350), (2,8), (5,20)]
+    res = minimize(error, initial, bounds=bounds, method='L-BFGS-B')
+    if res.success:
+        best_params = {'stroke_len': res.x[0], 'freq': res.x[1], 'tilt': res.x[2], 'wash_water': res.x[3]}
+        return best_params, res.fun
+    else:
+        return None, 1e6
+
+# =============================================
+# Main Simulation Function
+# =============================================
+def simulate(
+    feed_rate_tph,
+    gold_feed_gpt, ilmenite_feed_wt, rutile_feed_wt, monazite_feed_wt,
+    stroke_len, freq, tilt, wash_water,
+    mode,  # "forward" or "target"
+    target_gold_tph, target_ilmenite_tph, target_rutile_tph, target_monazite_tph
+):
+    # User-defined feed grades
+    feed_grades = {
+        'Gold': gold_feed_gpt * 1e-6,
+        'Ilmenite': ilmenite_feed_wt / 100,
+        'Rutile': rutile_feed_wt / 100,
+        'Monazite': monazite_feed_wt / 100,
+        'Gangue': 1 - (ilmenite_feed_wt + rutile_feed_wt + monazite_feed_wt)/100
+    }
+
+    if mode == "Forward Model (Adjust Parameters)":
+        params = {'stroke_len': stroke_len, 'freq': freq, 'tilt': tilt, 'wash_water': wash_water}
+        model_rec, yc, ym, yt, grades, rates = shaking_table_model(params, feed_grades, feed_rate_tph)
+
+        text = f"### Forward Model Results\n\n"
+        text += f"**Mass Yields:** Conc {yc:.1f}%, Midd {ym:.1f}%, Tail {yt:.1f}%\n\n"
+        text += "| Mineral   | Rec Conc (%) | Grade Conc     | Rate to Conc (tph) |\n"
+        text += "|-----------|--------------|----------------|--------------------|\n"
+        for m in minerals:
+            rec = model_rec[m]['Conc']
+            grade = grades[m]
+            unit = "g/t" if m=="Gold" else "wt%"
+            rate = rates[m]
+            text += f"| {m:<9} | {rec:11.1f} | {grade:13.2f} {unit} | {rate:17.3f} |\n"
+
+    else:  # Target mode
+        target_rates = {
+            'Gold': target_gold_tph,
+            'Ilmenite': target_ilmenite_tph,
+            'Rutile': target_rutile_tph,
+            'Monazite': target_monazite_tph
+        }
+        best_params, err = find_parameters_for_targets(target_rates, feed_grades, feed_rate_tph)
+        if best_params:
+            model_rec, yc, ym, yt, grades, rates = shaking_table_model(best_params, feed_grades, feed_rate_tph)
+            text = f"### Suggested Parameters to Achieve Target Rates (Error: {err:.1f})\n\n"
+            text += f"**Suggested:** Stroke {best_params['stroke_len']:.1f} mm, Freq {best_params['freq']:.0f}/min, Tilt {best_params['tilt']:.1f}°, Wash {best_params['wash_water']:.1f}\n\n"
+            text += "| Mineral   | Target Rate (tph) | Achieved Rate | Grade Conc     |\n"
+            text += "|-----------|-------------------|---------------|----------------|\n"
+            for m in minerals:
+                grade = grades[m]
+                unit = "g/t" if m=="Gold" else "wt%"
+                text += f"| {m:<9} | {target_rates[m]:17.3f} | {rates[m]:12.3f} | {grade:13.2f} {unit} |\n"
+        else:
+            text = "Could not find parameters to match targets."
+
+    # Plots
+    fig1 = plt.figure(figsize=(10,6))
+    x = np.arange(len(minerals))
+    width = 0.25
+    plt.bar(x, [model_rec[m]['Conc'] for m in minerals], width, label='Conc')
+    plt.bar(x + width, [model_rec[m]['Midd'] for m in minerals], width, label='Midd')
+    plt.bar(x + 2*width, [model_rec[m]['Tail'] for m in minerals], width, label='Tail')
+    plt.xlabel('Mineral')
+    plt.ylabel('Distribution (%)')
+    plt.title('Mineral Distribution Across Products')
+    plt.xticks(x + width, minerals)
+    plt.legend()
+    plt.grid(True, axis='y')
+
+    fig2 = plt.figure(figsize=(8,8))
+    labels = ['Conc', 'Midd', 'Tail']
+    sizes = [yc, ym, yt]
+    plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)
+    plt.title('Mass Yield Pie Chart')
+
+    # Simple estimated "cost" (arbitrary: lower yield higher cost due to more processing)
+    est_cost = 1000 + 500 * (yc / 30)  # dummy
+    text += f"\n\n**Estimated Relative Operating Cost:** ${est_cost:.0f} (arbitrary units)"
+
+    return text, fig1, fig2
 
-# ---------------- CALIBRATION ----------------
-initial = [0.7, 0.2, 0.55, 0.3, 0.6, 0.25, 0.65, 0.25]
-bounds = [(0, 1)] * len(initial)
+# =============================================
+# Gradio Interface with Sidebar Sections
+# =============================================
+with gr.Blocks(title="Advanced Shaking Table Digital Twin") as demo:
+    gr.Markdown("# Advanced Shaking Table Digital Twin - Attock Placer Plant")
+    gr.Markdown("Physical-inspired model with manual inputs for feed, operating parameters, and target rates.")
 
-solution = minimize(error_fn, initial, bounds=bounds, method="SLSQP")
-params = solution.x
+    mode = gr.Radio(["Forward Model (Adjust Parameters)", "Inverse: Find Parameters for Target Rates"], value="Forward Model (Adjust Parameters)", label="Mode")
 
-# ---------------- RESULTS ----------------
-rows = []
-res = shaking_table(params)
-for m in minerals:
-    rows.append([m] + res[m])
+    with gr.Row():
+        with gr.Column(scale=1):  # Sidebar-like
+            gr.Markdown("### 1. Feed Composition & Rate")
+            feed_rate_tph = gr.Slider(50, 500, value=300, step=10, label="Feed Rate (tph)")
+            gold_feed_gpt = gr.Number(value=3.74, label="Gold Feed Grade (g/t)")
+            ilmenite_feed_wt = gr.Number(value=5.625, label="Ilmenite Feed Grade (wt%)")
+            rutile_feed_wt = gr.Number(value=1.35, label="Rutile Feed Grade (wt%)")
+            monazite_feed_wt = gr.Number(value=0.6, label="Monazite Feed Grade (wt%)")
 
-df = pd.DataFrame(
-    rows,
-    columns=["Mineral", "Concentrate Recovery", "Middlings Recovery", "Tailings Recovery"]
-)
+            gr.Markdown("### 2. Operating Parameters")
+            stroke_len = gr.Slider(8, 25, value=18, step=0.5, label="Stroke Length (mm)")
+            freq = gr.Slider(250, 350, value=300, step=5, label="Stroke Frequency (/min)")
+            tilt = gr.Slider(2, 8, value=5, step=0.1, label="Deck Inclination (degrees)")
+            wash_water = gr.Slider(5, 20, value=12, step=0.5, label="Wash Water Flow (units)")
 
-st.subheader("Calibrated Recovery Results")
-st.dataframe(df)
+            gr.Markdown("### 3. Target Rates to Concentrate (tph) - for Inverse Mode")
+            target_gold_tph = gr.Number(value=0.8, label="Target Gold Rate to Conc (tph)")
+            target_ilmenite_tph = gr.Number(value=15, label="Target Ilmenite Rate (tph)")
+            target_rutile_tph = gr.Number(value=3.5, label="Target Rutile Rate (tph)")
+            target_monazite_tph = gr.Number(value=1.5, label="Target Monazite Rate (tph)")
 
-# ---------------- PLOT ----------------
-fig, ax = plt.subplots()
-df.set_index("Mineral").plot(kind="bar", ax=ax)
-ax.set_ylabel("Recovery Fraction")
-ax.set_title("Shaking Table Recovery Distribution")
-ax.grid(True)
+        with gr.Column(scale=3):
+            btn = gr.Button("Run Simulation", variant="primary")
+            output_text = gr.Markdown()
+            with gr.Row():
+                plot1 = gr.Plot(label="Mineral Distribution Bar Chart")
+                plot2 = gr.Plot(label="Mass Yield Pie Chart")
 
-st.pyplot(fig)
+    btn.click(
+        fn=simulate,
+        inputs=[feed_rate_tph, gold_feed_gpt, ilmenite_feed_wt, rutile_feed_wt, monazite_feed_wt,
+                stroke_len, freq, tilt, wash_water, mode,
+                target_gold_tph, target_ilmenite_tph, target_rutile_tph, target_monazite_tph],
+        outputs=[output_text, plot1, plot2]
+    )
 
-st.caption("Python Digital Twin | Attock Placer Plant | CEP")
+demo.launch()