Update app.py

app.py

@@ -5,35 +5,37 @@ import random
 
 # Data
 agents = {
-    "Doctors": 5,
-    "Nurses": 10,
-    "Clinicians": 8,
+    "Doctors": 10,
+    "Nurses": 4,
+    "Clinicians": 5,
     "Patients": 200
 }
 
-patient_conditions = ["Healthy", "Mild Illness", "Chronic Illness", "Emergency"]
-doctor_emotions = ["Calm", "Stressed", "Overwhelmed"]
-nurse_emotions = ["Focused", "Fatigued", "Panicked"]
-doctor_actions = ["Prescribe Medication", "Recommend Tests", "Consult Clinician", "Schedule Surgery"]
-nurse_actions = ["Monitor Vitals", "Administer Medication", "Report to Doctor", "Assist Surgery"]
-
-# Initial values for successful and unsuccessful treatments
-doctor_successful_treatments = random.randint(0, 10)
-doctor_unsuccessful_treatments = random.randint(0, 10)
-total_treatments = doctor_successful_treatments + doctor_unsuccessful_treatments
+# Patient conditions and distribution
+conditions = ["Healthy", "Mild Illness", "Chronic Illness", "Emergency"]
+condition_counts = {
+    "Healthy": 0,
+    "Mild Illness": 0,
+    "Chronic Illness": 0,
+    "Emergency": 0
+}
 
-# Randomly distribute patient conditions
-def get_patient_distribution():
-    conditions = {
-        "Healthy": random.randint(0, 100),
-        "Mild Illness": random.randint(0, 100),
-        "Chronic Illness": random.randint(0, 100),
-        "Emergency": random.randint(0, 100)
-    }
-    total = sum(conditions.values())
+# Create random patient condition distribution
+def distribute_patients():
+    remaining_patients = agents["Patients"]
     for condition in conditions:
-        conditions[condition] = int((conditions[condition] / total) * agents['Patients'])
-    return conditions
+        count = random.randint(0, remaining_patients)
+        condition_counts[condition] = count
+        remaining_patients -= count
+    condition_counts[conditions[-1]] += remaining_patients  # Add remaining patients to last condition
+
+# Randomly generate metrics for treatment
+def generate_treatment_metrics():
+    total_treatments = random.randint(50, 150)
+    successful_treatments = random.randint(0, total_treatments)
+    unsuccessful_treatments = total_treatments - successful_treatments
+    patients_waiting = agents["Patients"] - total_treatments
+    return total_treatments, successful_treatments, unsuccessful_treatments, patients_waiting
 
 # Create the gauge chart
 def create_gauge(title, value, max_value):
@@ -47,18 +49,6 @@ def create_gauge(title, value, max_value):
     fig.update_layout(template='plotly_dark')
     return fig
 
-# Create a function to update the gauge with different metrics
-def create_metrics_gauge(title, value, max_value):
-    fig = go.Figure()
-    fig.add_trace(go.Indicator(
-        mode="gauge+number",
-        value=value,
-        title={'text': title},
-        gauge={'axis': {'range': [0, max_value]}}
-    ))
-    fig.update_layout(template='plotly_dark')
-    return fig
-
 # Button to start the animation
 start_button = st.button("Start Animation")
 
@@ -66,45 +56,59 @@ if start_button:
     # Placeholder for dynamic updates
     placeholder = st.empty()
 
-    # Animation for patient conditions
-    for _ in range(1):
-        conditions = get_patient_distribution()
-        with placeholder.container():
-            st.subheader("Patient Conditions")
-            for condition, count in conditions.items():
-                fig = create_gauge(f"{condition} Patients", count, agents['Patients'])
-                st.plotly_chart(fig, use_container_width=True)
-                time.sleep(2)
-
-    # Animation for doctor emotions
-    for _ in range(1):
-        with placeholder.container():
-            st.subheader("Doctor Emotions")
-            for emotion in doctor_emotions:
-                fig = create_gauge(f"Doctor Emotion: {emotion}", random.randint(0, 100), 100)
-                st.plotly_chart(fig, use_container_width=True)
-                time.sleep(2)
-
-    # Animation for nurse emotions
-    for _ in range(1):
-        with placeholder.container():
-            st.subheader("Nurse Emotions")
-            for emotion in nurse_emotions:
-                fig = create_gauge(f"Nurse Emotion: {emotion}", random.randint(0, 100), 100)
-                st.plotly_chart(fig, use_container_width=True)
-                time.sleep(2)
-
-    # Animation for successful and unsuccessful treatments
-    for _ in range(1):
-        with placeholder.container():
-            st.subheader("Doctor Treatments")
-            fig = create_metrics_gauge("Successful Treatments", doctor_successful_treatments, total_treatments)
+    # Animation 1: Total Number of Agents
+    with placeholder.container():
+        st.subheader("Total Number of Agents")
+        for agent_type, count in agents.items():
+            fig = create_gauge(f"{agent_type}", count, 200)
             st.plotly_chart(fig, use_container_width=True)
             time.sleep(2)
 
-            fig = create_metrics_gauge("Unsuccessful Treatments", doctor_unsuccessful_treatments, total_treatments)
+    # Animation 2: Distribution of Patients by Condition
+    distribute_patients()
+    with placeholder.container():
+        st.subheader("Patient Conditions Distribution")
+        for condition, count in condition_counts.items():
+            fig = create_gauge(f"Patients with {condition}", count, agents["Patients"])
             st.plotly_chart(fig, use_container_width=True)
             time.sleep(2)
 
-    # Display a final summary
-    st.write("**All animations complete: Healthcare agents' metrics have been updated.**")
+    # Animation 3: Number of Patients Treated
+    total_treatments, successful_treatments, unsuccessful_treatments, patients_waiting = generate_treatment_metrics()
+    with placeholder.container():
+        st.subheader("Patients Treated by Doctors")
+        fig = create_gauge("Total Treatments", total_treatments, agents["Patients"])
+        st.plotly_chart(fig, use_container_width=True)
+        time.sleep(2)
+
+    # Animation 4: Number of Successful Treatments
+    with placeholder.container():
+        st.subheader("Successful Treatments")
+        fig = create_gauge("Successful Treatments", successful_treatments, total_treatments)
+        st.plotly_chart(fig, use_container_width=True)
+        time.sleep(2)
+
+    # Animation 5: Number of Unsuccessful Treatments
+    with placeholder.container():
+        st.subheader("Unsuccessful Treatments")
+        fig = create_gauge("Unsuccessful Treatments", unsuccessful_treatments, total_treatments)
+        st.plotly_chart(fig, use_container_width=True)
+        time.sleep(2)
+
+    # Animation 6: Number of Patients Waiting for Treatment
+    with placeholder.container():
+        st.subheader("Patients Waiting for Treatment")
+        fig = create_gauge("Patients Waiting", patients_waiting, agents["Patients"])
+        st.plotly_chart(fig, use_container_width=True)
+        time.sleep(2)
+
+    # Animation 7: Remaining Metrics (Optional)
+    remaining_metrics = agents["Patients"] - (total_treatments + patients_waiting)
+    with placeholder.container():
+        st.subheader("Remaining Metrics")
+        fig = create_gauge("Remaining Metrics", remaining_metrics, agents["Patients"])
+        st.plotly_chart(fig, use_container_width=True)
+        time.sleep(2)
+
+    # Final summary
+    st.write("**All animations complete: Healthcare metrics have been updated.**")