Update app.py

app.py

@@ -3,73 +3,68 @@ import numpy as np
 
 def calculate_tia(t1, a1, t2, a2):
     try:
-        # حساب معدل الإفراغ k
+        # Calculate washout rate constant (k)
         k = np.log(a1 / a2) / (t2 - t1)
-        # حساب النشاط الابتدائي A₀
+        # Estimate initial activity (A₀)
         a0 = a1 / np.exp(-k * t1)
-        # حساب النشاط التكاملي TIA
+        # Calculate Time-Integrated Activity (TIA)
         tia = a0 / k
-        result = f"**Results:**\n- A₀ = {a0:.2f} MBq\n- k = {k:.5f} per hour\n- TIA = {tia:.2f} MBq·h"
-    except:
-        result = "Error: Please enter valid numbers."
+
+        result = f"""
+        ### ✅ Results
+        - **A₀ (initial activity):** {a0:.2f} MBq  
+        - **k (washout rate):** {k:.5f} / hour  
+        - **TIA (time-integrated activity):** {tia:.2f} MBq·h  
+        """
+    except Exception:
+        result = "⚠️ Error: Please check your inputs. Values must be positive and non-zero."
 
     return result
 
 with gr.Blocks() as demo:
-    gr.Markdown("#gr.Markdown("""
----
-
-### ℹ️ **What Is This Tool?**
+    gr.Markdown("# ⚛️ Lu-177 Dosimetry Tool\nEstimate Time-Integrated Activity (TIA) from Two SPECT Time Points")
 
-This free, open-source web application is designed to calculate **Time-Integrated Activity (TIA)** for patients undergoing **Lutetium-177 PSMA therapy** in metastatic prostate cancer. It uses only two post-treatment SPECT imaging time points to produce accurate, personalized dosimetry in resource-constrained settings.
+    with gr.Row():
+        t1 = gr.Number(label="Time Point 1 (hours)", value=20)
+        a1 = gr.Number(label="Activity at Time 1 (MBq)", value=12)
 
-The tool relies on a simplified yet clinically validated mathematical model (mono-exponential washout), commonly used for organs with regular clearance kinetics such as kidneys or PSMA-positive tumors.
+    with gr.Row():
+        t2 = gr.Number(label="Time Point 2 (hours)", value=60)
+        a2 = gr.Number(label="Activity at Time 2 (MBq)", value=4)
 
----
+    output = gr.Markdown()
+    button = gr.Button("Calculate TIA")
+    button.click(fn=calculate_tia, inputs=[t1, a1, t2, a2], outputs=output)
 
-### ⚙️ **How Does It Work?**
+    gr.Markdown("""
+    ---
+    ### ℹ️ What Is This Tool?
 
-You simply enter:
-- Two imaging time points (in hours)
-- Measured activity for each point (in MBq)
+    This open-source calculator estimates **Time-Integrated Activity (TIA)** for patients receiving **Lutetium-177 PSMA therapy** for metastatic prostate cancer.  
+    It uses **two post-treatment SPECT/CT time points** to fit a simplified **mono-exponential washout model**, helping clinicians perform dose calculations with minimal imaging.
 
-The tool automatically:
-- Fits the time–activity curve (TAC) using exponential modeling  
-- Calculates the washout rate (_k_), initial activity (_A₀_), and total time-integrated activity (_TIA = A₀ / k_)  
-- Displays results instantly and clearly  
+    ---
+    ### ⚙️ How It Works
 
----
+    You enter two scan times (hours) and their corresponding measured activity (MBq).  
+    The tool automatically:
 
-### 🧠 **Scientific Basis**
+    - Fits an exponential time–activity curve  
+    - Calculates the washout rate (_k_)  
+    - Estimates initial activity (_A₀_)  
+    - Computes **TIA = A₀ / k**, which can be used in absorbed dose estimation
 
-This method applies the following model:
+    ---
+    ### 🧠 Scientific Basis
 
-> **A(t) = A₀ · exp(–k · t)**  
-> **TIA = A₀ / k**
+    Model used:
 
-Where:
-- _A(t)_ is the activity at time _t_  
-- _A₀_ is the estimated initial activity  
-- _k_ is the washout rate (clearance constant)  
-- _TIA_ is the integrated activity over time used to estimate absorbed radiation dose
+    > A(t) = A₀ · exp(–k · t)  
+    > TIA = A₀ / k
 
-This model is suitable for organs and lesions that exhibit predictable biological clearance and helps support practical dosimetry protocols in clinical or academic environments.
+    This approach is ideal for organs with regular clearance kinetics (e.g., kidneys) or tumors with predictable washout, and is based on principles used in MIRD dosimetry and EANM guidelines.
 
----
-""")
-.")
-    
-    with gr.Row():
-        t1 = gr.Number(label="Time Point 1 (hours)", value=20)
-        a1 = gr.Number(label="Activity at t1 (MBq)", value=12)
-    
-    with gr.Row():
-        t2 = gr.Number(label="Time Point 2 (hours)", value=60)
-        a2 = gr.Number(label="Activity at t2 (MBq)", value=4)
+    ---
+    """)
     
-    button = gr.Button("Calculate TIA")
-    output = gr.Markdown()
-
-    button.click(fn=calculate_tia, inputs=[t1, a1, t2, a2], outputs=output)
-
 demo.launch()