Update app.py

app.py

@@ -13,6 +13,10 @@ agents = {
 # Define states for patients (health conditions)
 patient_conditions = ["Healthy", "Mild Illness", "Chronic Illness", "Emergency"]
 
+# Define emotions for healthcare agents (stress, focus, etc.)
+doctor_emotions = ["Calm", "Stressed", "Overwhelmed"]
+nurse_emotions = ["Focused", "Fatigued", "Panicked"]
+
 # Define the actions doctors can take
 doctor_actions = ["Prescribe Medication", "Recommend Tests", "Consult Clinician", "Schedule Surgery"]
 
@@ -37,9 +41,19 @@ rewards = {
 penalties = {
     "Wrong Medication": -10,
     "Missed Diagnosis": -20,
-    "Incorrect Test Recommendation": -5
+    "Incorrect Test Recommendation": -5,
+    "Stress-Induced Mistake": -15
+}
+
+# Track doctor and nurse performance metrics
+performance_metrics = {
+    "Doctor": {"successful_treatments": 0, "failed_treatments": 0},
+    "Nurse": {"successful_assists": 0, "failed_assists": 0}
 }
 
+# Track patient satisfaction (out of 100)
+patient_satisfaction = 100
+
 # Define Reinforcement Learning algorithm (basic Q-learning approach)
 class Agent:
     def __init__(self, agent_type):
@@ -64,16 +78,31 @@ doctor_agent = Agent("Doctor")
 nurse_agent = Agent("Nurse")
 
 # Streamlit UI
-st.title("Healthcare Civilization - Multi-Agent Simulation")
+st.title("Enhanced Healthcare Civilization - Multi-Agent Simulation")
 
 st.write(f"Current number of agents in the system:")
 for agent, count in agents.items():
     st.write(f"{agent}: {count}")
 
+# Simulate special event (disease outbreak or resource shortage)
+special_event = random.choice([None, "Disease Outbreak", "Resource Shortage"])
+if special_event:
+    st.subheader(f"Special Event: {special_event}")
+    if special_event == "Disease Outbreak":
+        st.write("A sudden disease outbreak has flooded the hospital with new patients. Resources are limited!")
+    elif special_event == "Resource Shortage":
+        st.write("A medical supply shortage is impacting the hospital. Staff must prioritize high-risk patients.")
+
 # Patient Condition Simulation
 patient_state = random.choice(patient_conditions)
 st.write(f"Simulated Patient Condition: {patient_state}")
 
+# Random doctor and nurse emotions (affects performance)
+doctor_emotion = random.choice(doctor_emotions)
+nurse_emotion = random.choice(nurse_emotions)
+st.write(f"Doctor's Emotional State: {doctor_emotion}")
+st.write(f"Nurse's Emotional State: {nurse_emotion}")
+
 # Doctor Action Simulation
 doctor_action = doctor_agent.choose_action()
 st.write(f"Doctor's Chosen Action: {doctor_actions[doctor_action]}")
@@ -82,18 +111,49 @@ st.write(f"Doctor's Chosen Action: {doctor_actions[doctor_action]}")
 nurse_action = nurse_agent.choose_action()
 st.write(f"Nurse's Chosen Action: {nurse_actions[nurse_action]}")
 
-# Reward or penalty based on action
+# Complications that can arise during treatment
+complication = random.choice([None, "Allergic Reaction", "Unexpected Complication"])
+if complication:
+    st.subheader(f"Complication: {complication}")
+    if complication == "Allergic Reaction":
+        st.write("The patient has developed an allergic reaction to the prescribed medication!")
+        penalty = penalties.get("Wrong Medication", 0)
+        patient_satisfaction -= 10
+    elif complication == "Unexpected Complication":
+        st.write("An unexpected complication occurred during surgery!")
+        penalty = penalties.get("Stress-Induced Mistake", 0)
+        patient_satisfaction -= 15
+else:
+    penalty = 0
+
+# Reward or penalty based on action and emotional state
 reward = rewards.get(doctor_actions[doctor_action], 0)
-penalty = penalties.get("Wrong Medication" if doctor_action == 0 and patient_state == "Healthy" else "", 0)
+if doctor_emotion in ["Stressed", "Overwhelmed"]:
+    penalty += penalties.get("Stress-Induced Mistake", 0)
 
 # Update Q-values
 doctor_agent.update_q_value(patient_conditions.index(patient_state), doctor_action, reward if penalty == 0 else penalty)
 
 st.write(f"Doctor's Reward: {reward if penalty == 0 else penalty}")
+st.write(f"Patient Satisfaction: {patient_satisfaction}")
 
 # Simulated patient feedback and next steps
 next_step = random.choice(["Recovery", "Further Treatment Needed", "Complication"])
 st.write(f"Patient Status after treatment: {next_step}")
 
+# Performance tracking
+if next_step == "Recovery":
+    performance_metrics["Doctor"]["successful_treatments"] += 1
+    performance_metrics["Nurse"]["successful_assists"] += 1
+else:
+    performance_metrics["Doctor"]["failed_treatments"] += 1
+    performance_metrics["Nurse"]["failed_assists"] += 1
+
 st.write("Simulation completed! Run again to simulate different actions and outcomes.")
 
+# Display performance metrics
+st.subheader("Performance Metrics:")
+st.write(f"Doctor's Successful Treatments: {performance_metrics['Doctor']['successful_treatments']}")
+st.write(f"Doctor's Failed Treatments: {performance_metrics['Doctor']['failed_treatments']}")
+st.write(f"Nurse's Successful Assists: {performance_metrics['Nurse']['successful_assists']}")
+st.write(f"Nurse's Failed Assists: {performance_metrics['Nurse']['failed_assists']}")