Update app.py

app.py

@@ -1,29 +1,99 @@
-import json
 import streamlit as st
+import numpy as np
+import random
 
-# Load the JSON file
-with open('prompt_configure.json', 'r') as file:
-    role_data = json.load(file)
+# Initialize agents (doctors, nurses, clinicians, patients)
+agents = {
+    "Doctors": 5,
+    "Nurses": 10,
+    "Clinicians": 8,
+    "Patients": 200
+}
 
-st.title("PrecisionCare Agent Interaction System")
+# Define states for patients (health conditions)
+patient_conditions = ["Healthy", "Mild Illness", "Chronic Illness", "Emergency"]
 
-# Select agent type
-agent_type = st.selectbox("Select an agent type:", role_data['roles'].keys())
+# Define the actions doctors can take
+doctor_actions = ["Prescribe Medication", "Recommend Tests", "Consult Clinician", "Schedule Surgery"]
 
-# Get the selected agent's data
-agent_info = role_data['roles'][agent_type]
-st.write(f"Interacting with {agent_info['name']}")
+# Define the actions nurses can take
+nurse_actions = ["Monitor Vitals", "Administer Medication", "Report to Doctor", "Assist Surgery"]
 
-# Multiple prompt options
-prompt_options = st.multiselect("Select tasks to perform:", [task['task'] for task in agent_info['prompts']])
+# Define the actions clinicians can take
+clinician_actions = ["Review Diagnostic Test", "Consult with Doctor", "Recommend Additional Tests"]
 
-# Display input fields for selected tasks
-for prompt in prompt_options:
-    task_info = next(item for item in agent_info['prompts'] if item['task'] == prompt)
-    if task_info['input_type'] == 'textarea':
-        user_input = st.text_area(task_info['input_label'])
-        st.write(f"{agent_info['name']} - {task_info['task']}: {user_input}")
+# Reward and penalty system (simplified for Reinforcement Learning concept)
+rewards = {
+    "Prescribe Medication": 10,
+    "Recommend Tests": 5,
+    "Consult Clinician": 7,
+    "Schedule Surgery": 15,
+    "Monitor Vitals": 3,
+    "Administer Medication": 10,
+    "Review Diagnostic Test": 6,
+    "Recommend Additional Tests": 4
+}
+
+penalties = {
+    "Wrong Medication": -10,
+    "Missed Diagnosis": -20,
+    "Incorrect Test Recommendation": -5
+}
+
+# Define Reinforcement Learning algorithm (basic Q-learning approach)
+class Agent:
+    def __init__(self, agent_type):
+        self.agent_type = agent_type
+        self.q_table = np.zeros((len(patient_conditions), len(doctor_actions if agent_type == "Doctor" else nurse_actions)))
+        self.state = random.choice(patient_conditions)
+    
+    def choose_action(self, exploration_rate=0.1):
+        if random.uniform(0, 1) < exploration_rate:  # Explore
+            return random.randint(0, len(doctor_actions)-1 if self.agent_type == "Doctor" else len(nurse_actions)-1)
+        else:  # Exploit (choose the best action based on Q-values)
+            return np.argmax(self.q_table)
+    
+    def update_q_value(self, state, action, reward, learning_rate=0.1, discount_factor=0.95):
+        old_q_value = self.q_table[state, action]
+        best_future_q_value = np.max(self.q_table)
+        new_q_value = old_q_value + learning_rate * (reward + discount_factor * best_future_q_value - old_q_value)
+        self.q_table[state, action] = new_q_value
+
+# Instantiate agents
+doctor_agent = Agent("Doctor")
+nurse_agent = Agent("Nurse")
+
+# Streamlit UI
+st.title("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}")
+
+# Patient Condition Simulation
+patient_state = random.choice(patient_conditions)
+st.write(f"Simulated Patient Condition: {patient_state}")
+
+# Doctor Action Simulation
+doctor_action = doctor_agent.choose_action()
+st.write(f"Doctor's Chosen Action: {doctor_actions[doctor_action]}")
+
+# Nurse Action Simulation
+nurse_action = nurse_agent.choose_action()
+st.write(f"Nurse's Chosen Action: {nurse_actions[nurse_action]}")
+
+# Reward or penalty based on action
+reward = rewards.get(doctor_actions[doctor_action], 0)
+penalty = penalties.get("Wrong Medication" if doctor_action == 0 and patient_state == "Healthy" else "", 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}")
+
+# Simulated patient feedback and next steps
+next_step = random.choice(["Recovery", "Further Treatment Needed", "Complication"])
+st.write(f"Patient Status after treatment: {next_step}")
+
+st.write("Simulation completed! Run again to simulate different actions and outcomes.")
 
-# Submit the interaction
-if st.button("Submit"):
-    st.write(f"Interactions for {agent_type} have been submitted.")