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import streamlit as st | |
def triage_checkin(): | |
st.write("### Triage and Check-in Expert π") | |
for i in range(1, 4): | |
st.text_input(f"Question {i} for Triage") | |
def lab_analyst(): | |
st.write("### Lab Analyst π§ͺ") | |
for i in range(1, 4): | |
st.text_input(f"Question {i} for Lab Analysis") | |
def medicine_specialist(): | |
st.write("### Medicine Specialist π") | |
for i in range(1, 4): | |
st.text_input(f"Question {i} for Medicine") | |
def service_expert(): | |
st.write("### Service Expert π²") | |
for i in range(1, 4): | |
st.text_input(f"Question {i} for Service") | |
def care_expert(): | |
st.write("### Level of Care Expert π₯") | |
for i in range(1, 4): | |
st.text_input(f"Question {i} for Level of Care") | |
def terminology_expert(): | |
st.write("### Terminology Expert π") | |
for i in range(1, 4): | |
st.text_input(f"Question {i} for Terminology") | |
def cmo(): | |
st.write("### Chief Medical Officer π©Ί") | |
for i in range(1, 4): | |
st.text_input(f"Question {i} for CMO") | |
def medical_director(): | |
st.write("### Medical Director Team π’") | |
for i in range(1, 4): | |
st.text_input(f"Question {i} for Medical Director") | |
def main(): | |
st.title("Mixture of Medical Experts Model") | |
st.write("Harness the power of AI with this specialized healthcare framework! π") | |
role = st.selectbox("Select AI Role:", [ | |
"Triage and Check-in Expert", | |
"Lab Analyst", | |
"Medicine Specialist", | |
"Service Expert", | |
"Level of Care Expert", | |
"Terminology Expert", | |
"Chief Medical Officer", | |
"Medical Director Team" | |
]) | |
if role == "Triage and Check-in Expert": | |
triage_checkin() | |
elif role == "Lab Analyst": | |
lab_analyst() | |
elif role == "Medicine Specialist": | |
medicine_specialist() | |
elif role == "Service Expert": | |
service_expert() | |
elif role == "Level of Care Expert": | |
care_expert() | |
elif role == "Terminology Expert": | |
terminology_expert() | |
elif role == "Chief Medical Officer": | |
cmo() | |
elif role == "Medical Director Team": | |
medical_director() | |
# Define Roles and their Descriptions | |
roles = { | |
"1. Coder": "π» Creates short python code functions to solve tasks.", | |
"2. Humanities Expert": "π Focuses on arts, literature, history, and other humanities subjects.", | |
"3. Analyst": "π€ Analyzes situations and provides logical solutions.", | |
"4. Roleplay Expert": "π Specialized in mimicking behaviors or characters.", | |
"5. Mathematician": "β Solves mathematical problems with precision.", | |
"6. STEM Expert": "π¬ Specialized in Science, Technology, Engineering, and Mathematics tasks.", | |
"7. Extraction Expert": "π Strictly sticks to facts and extracts concise information.", | |
"8. Drafter": "π Exhibits expertise in generating textual content and narratives.", | |
} | |
# Streamlit UI | |
st.title("AI Role Selector - CHARMSED π€β¨") | |
st.markdown(""" | |
### Harness the power of AI with the CHARMSED framework. | |
#### This suite of roles brings together a comprehensive set of AI capabilities, tailored for diverse tasks: | |
- **C**oder π»: Craft pythonic solutions with precision. | |
- **H**umanities Expert π: Dive deep into arts, literature, and history. | |
- **A**nalyst π€: Derive insights through logical reasoning. | |
- **R**oleplay Expert π: Mimic behaviors or adopt personas for engaging interactions. | |
- **M**athematician β: Crunch numbers and solve mathematical enigmas. | |
- **S**TEM Expert π¬: Navigate through the realms of Science, Technology, Engineering, and Mathematics. | |
- **E**xtraction Expert π: Extract concise information with a laser-focus. | |
- **D**rafter π: Generate textual content and narratives with flair. | |
Empower your tasks with the perfect AI role and unleash the magic of CHARMSED! | |
""") | |
# Dropdown to select role | |
selected_role = st.selectbox("Select AI Role:", list(roles.keys())) | |
# Display the description of the selected role | |
st.write(roles[selected_role]) | |
# Switch to choose between two models | |
model = st.radio("Choose Model:", ["model_1", "model_2"]) | |
# Text area for user input | |
user_input = st.text_area("Provide your task/question:") | |
# Button to execute | |
if st.button("Execute"): | |
# Here, you would add code to get the AI response based on the selected role and model. | |
# For now, just echoing the user input. | |
st.write(f"You said: {user_input}") | |
if __name__ == "__main__": | |
main() | |