Update app.py

app.py

@@ -1,92 +1,83 @@
 import gradio as gr
-import datetime
 import numpy as np
 import matplotlib.pyplot as plt
 
-# Function to calculate predicted blood glucose based on inputs
-def predict_glucose(blood_glucose_now, last_meal_time, meal_type, carb_amount, medication_taken, medication_time, exercise_done, exercise_duration):
-    # Convert input times (12-hour format) to datetime objects
-    last_meal_time = datetime.datetime.strptime(last_meal_time, "%I:%M %p")
-    medication_time = datetime.datetime.strptime(medication_time, "%I:%M %p") if medication_taken else None
-
-    # Set estimated glucose impact values for different meal types
-    meal_effects = {
-        "Fast Carbs (Juice, Milk)": 30,
-        "Slow Carbs (Whole Grains)": 10,
-        "High-Protein Meal": 5,
-        "High-Fat Meal": 5,
-        "Balanced Meal": 15,
-        "No Meal": 0
-    }
-
-    # Determine effect of the meal type
-    glucose_change = meal_effects.get(meal_type, 0)
-
-    # Calculate time since medication was taken
-    if medication_taken and medication_time:
-        time_since_medication = (datetime.datetime.now() - medication_time).total_seconds() / 3600
-        if 1 <= time_since_medication <= 3:
-            glucose_change -= 20  # Approximate glucose decrease due to Galvus effect
-
-    # Adjust for exercise duration
-    if exercise_done and exercise_duration:
-        glucose_change -= min(exercise_duration // 10 * 5, 30)  # Reduces 5 mg/dL per 10 min, max 30 mg/dL
-
-    # Predict blood glucose levels
-    glucose_now = blood_glucose_now + glucose_change
-    glucose_1hr = glucose_now + glucose_change * 0.5
-    glucose_3hr = glucose_now + glucose_change * 0.3
-
-    # Determine risk levels
-    def get_risk_label(glucose):
-        if glucose > 180:
-            return "⚠️ Hyperglycemia Risk"
-        elif glucose < 70:
-            return "⚠️ Hypoglycemia Risk"
-        else:
-            return "Normal"
-
-    risk_now = get_risk_label(glucose_now)
-    risk_1hr = get_risk_label(glucose_1hr)
-    risk_3hr = get_risk_label(glucose_3hr)
-
-    # Plot blood glucose trajectory
-    time_points = [0, 1, 3]  # Hours
-    glucose_values = [glucose_now, glucose_1hr, glucose_3hr]
-
-    plt.figure(figsize=(8, 6))
-    plt.plot(time_points, glucose_values, marker='o', color='b', label="Predicted Glucose")
-    plt.title('Blood Glucose Trajectory Over Time')
-    plt.xlabel('Time (hours)')
-    plt.ylabel('Blood Glucose (mg/dL)')
+# Function to simulate blood glucose changes over time
+def predict_glucose(current_glucose, meal_type, meal_time, galvus_dose, exercise_duration, fast_carbs_ml, prediction_time=3):
+    # Constants for glucose reduction effects
+    post_meal_reduction = 63.6  # mg/dL (avg reduction for Vildagliptin in 2 hours)
+    fasting_reduction = 27.7  # mg/dL (avg reduction over 6-12 hours)
+    
+    # Adjust for fast carbs (milk, juice, etc.)
+    carb_effect = fast_carbs_ml * 1.5  # Approximate glucose rise per mL of fast carbs
+
+    # Calculate blood glucose over time considering meal type, Galvus dose, and exercise
+    if meal_type == 'High-carb':
+        glucose_after_meal = current_glucose + carb_effect
+    else:
+        glucose_after_meal = current_glucose
+
+    # Simulate blood glucose levels over 1 hour and 3 hours
+    glucose_1hr = glucose_after_meal - post_meal_reduction + carb_effect * 0.5  # Adjust for carb effect
+    glucose_3hr = glucose_1hr - fasting_reduction
+
+    # Apply Galvus pharmacokinetic effects
+    if galvus_dose > 0:
+        glucose_3hr -= fasting_reduction  # Galvus effect after 3 hours
+
+    # Exercise effect on glucose (hypothetical value, may vary based on intensity)
+    glucose_3hr -= exercise_duration * 2  # Exercise reduces glucose by 2 mg/dL per minute
+
+    # Plotting the graph of glucose prediction over time
+    time_points = [0, 1, 3]  # Time: 0 hours, 1 hour, 3 hours
+    glucose_values = [current_glucose, glucose_1hr, glucose_3hr]
+
+    plt.plot(time_points, glucose_values, marker='o', color='b')
+    plt.title("Blood Glucose Prediction Over Time")
+    plt.xlabel("Time (Hours)")
+    plt.ylabel("Blood Glucose (mg/dL)")
+    plt.xticks([0, 1, 2, 3])
     plt.grid(True)
-    plt.axhline(y=180, color='r', linestyle='--', label="Hyperglycemia Threshold")
-    plt.axhline(y=70, color='g', linestyle='--', label="Hypoglycemia Threshold")
-    plt.legend(loc="best")
-
-    # Save the graph
-    plt_file_path = "/tmp/glucose_trajectory.png"
-    plt.savefig(plt_file_path)
-
-    # Output text summary
-    result_text = f"Now: {glucose_now:.1f} mg/dL → {risk_now}\nIn 1 Hour: {glucose_1hr:.1f} mg/dL → {risk_1hr}\nIn 3 Hours: {glucose_3hr:.1f} mg/dL → {risk_3hr}"
-
-    return result_text, plt_file_path
-
-# Gradio interface
-iface = gr.Interface(fn=predict_glucose,
-                     inputs=[
-                         gr.Number(label="Current Blood Glucose (mg/dL)"),
-                         gr.Textbox(label="Last Meal Time (e.g., 12:00 AM)"),
-                         gr.Radio(label="Meal Type", choices=[
-                             "Fast Carbs (Juice, Milk)", "Slow Carbs (Whole Grains)",
-                             "High-Protein Meal", "High-Fat Meal", "Balanced Meal", "No Meal"]),
-                         gr.Number(label="Carb Amount (grams)"),
-                         gr.Checkbox(label="Medication Taken (Galvus)"),
-                         gr.Textbox(label="Medication Time (e.g., 12:00 AM)"),
-                         gr.Checkbox(label="Exercise Done"),
-                         gr.Number(label="Exercise Duration (minutes)")
-                     ],
-                     outputs=[gr.Textbox(), gr.Image(type="filepath")])
+    plt.tight_layout()
+
+    # Save the graph as a file to show it in Gradio
+    plt.savefig('/tmp/blood_glucose_prediction.png')
+    plt.close()
+
+    # Return glucose predictions and the image file path
+    return glucose_1hr, glucose_3hr, '/tmp/blood_glucose_prediction.png'
+
+# Gradio Interface
+def build_interface():
+    with gr.Blocks() as iface:
+        gr.Markdown("# Blood Glucose Prediction Model (With Vildagliptin Effects)")
+
+        # Inputs for current glucose, meal info, medication dose, exercise, fast carbs
+        with gr.Row():
+            current_glucose = gr.Number(label="Current Blood Glucose (mg/dL)", value=150)
+            meal_type = gr.Radio(choices=["Normal", "High-carb"], label="Meal Type", value="Normal")
+            meal_time = gr.Number(label="Last Meal Time (in hours)", value=2)
+            galvus_dose = gr.Number(label="Galvus Dose (mg)", value=50)
+            exercise_duration = gr.Number(label="Exercise Duration (min)", value=0)
+            fast_carbs_ml = gr.Number(label="Fast Carbs (mL)", value=0)
+
+        # Output predictions and graph
+        glucose_1hr_output = gr.Textbox(label="Predicted Glucose Level in 1 Hour (mg/dL)")
+        glucose_3hr_output = gr.Textbox(label="Predicted Glucose Level in 3 Hours (mg/dL)")
+        glucose_graph = gr.Image(label="Blood Glucose Prediction Graph")
+
+        # Button to trigger prediction
+        predict_button = gr.Button("Predict Blood Glucose")
+
+        # Set button action
+        predict_button.click(
+            predict_glucose,
+            inputs=[current_glucose, meal_type, meal_time, galvus_dose, exercise_duration, fast_carbs_ml],
+            outputs=[glucose_1hr_output, glucose_3hr_output, glucose_graph]
+        )
+
+    return iface
 
+# Build and launch the Gradio interface
+iface = build_interface()
 iface.launch()