Spaces:
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Update app.py
Browse files
app.py
CHANGED
@@ -8,13 +8,14 @@ from docx import Document
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from io import BytesIO
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import base64
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load_dotenv()
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#
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os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
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os.environ["SERPER_API_KEY"] = os.getenv("SERPER_API_KEY")
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def generate_docx(result):
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doc = Document()
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doc.add_heading('Healthcare Diagnosis and Treatment Recommendations', 0)
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def get_download_link(bio, filename):
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b64 = base64.b64encode(bio.read()).decode()
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return f'<a href="data:application/vnd.openxmlformats-officedocument.wordprocessingml.document;base64,{b64}" download="{filename}">Download
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st.set_page_config(
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)
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#
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st.
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search_tool = SerperDevTool()
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scrape_tool = ScrapeWebsiteTool()
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@@ -56,7 +145,7 @@ llm = ChatOpenAI(
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diagnostician = Agent(
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role="Medical Diagnostician",
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goal="Analyze patient symptoms and medical history to provide a preliminary diagnosis.",
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backstory="
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verbose=True,
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allow_delegation=False,
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tools=[search_tool, scrape_tool],
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@@ -65,8 +154,8 @@ diagnostician = Agent(
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treatment_advisor = Agent(
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role="Treatment Advisor",
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goal="Recommend appropriate treatment plans based on the diagnosis
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backstory="
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verbose=True,
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allow_delegation=False,
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tools=[search_tool, scrape_tool],
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# Define Tasks
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diagnose_task = Task(
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description=(
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"1. Analyze the patient's symptoms ({symptoms}) and medical history ({medical_history}).\n"
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"2.
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"3.
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),
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expected_output="A preliminary diagnosis with
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agent=diagnostician
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)
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treatment_task = Task(
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description=(
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"1. Based on the diagnosis,
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"2. Consider
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"3.
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),
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expected_output="A comprehensive treatment plan
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agent=treatment_advisor
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)
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# Create Crew
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crew = Crew(
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agents=[diagnostician, treatment_advisor],
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tasks=[diagnose_task, treatment_task],
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verbose=True
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)
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from io import BytesIO
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import base64
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# Load environment variables
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load_dotenv()
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# Configure API keys
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os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
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os.environ["SERPER_API_KEY"] = os.getenv("SERPER_API_KEY")
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# Helper Functions
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def generate_docx(result):
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doc = Document()
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doc.add_heading('Healthcare Diagnosis and Treatment Recommendations', 0)
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def get_download_link(bio, filename):
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b64 = base64.b64encode(bio.read()).decode()
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return f'<a href="data:application/vnd.openxmlformats-officedocument.wordprocessingml.document;base64,{b64}" download="{filename}" class="download-button">Download Report</a>'
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# Page Configuration
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st.set_page_config(
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page_title="Medical AI Assistant",
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page_icon="π₯",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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# Custom CSS
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st.markdown("""
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<style>
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.main {
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padding: 2rem;
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}
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.stButton > button {
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width: 100%;
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background-color: #007bff;
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color: white;
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padding: 0.5rem 1rem;
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border-radius: 0.5rem;
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border: none;
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margin-top: 1rem;
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}
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.stButton > button:hover {
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background-color: #0056b3;
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}
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.download-button {
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display: inline-block;
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padding: 0.5rem 1rem;
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background-color: #28a745;
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color: white;
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text-decoration: none;
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border-radius: 0.5rem;
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margin-top: 1rem;
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}
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.download-button:hover {
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background-color: #218838;
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color: white;
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}
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.stTextInput > div > div > input {
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border-radius: 0.5rem;
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}
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.stTextArea > div > div > textarea {
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border-radius: 0.5rem;
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}
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</style>
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""", unsafe_allow_html=True)
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# Sidebar for Patient Information
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with st.sidebar:
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st.image("https://img.icons8.com/color/96/000000/healthcare.png", width=100)
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st.title("Patient Information")
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with st.form("patient_info"):
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gender = st.selectbox('Gender', ('Male', 'Female', 'Other'))
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age = st.number_input('Age', min_value=0, max_value=120, value=25)
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height = st.number_input('Height (cm)', min_value=0, max_value=300, value=170)
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weight = st.number_input('Weight (kg)', min_value=0, max_value=500, value=70)
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submit_button = st.form_submit_button("Save Patient Info")
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# Main Content
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st.title("π₯ Medical AI Assistant")
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st.markdown("""
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<div style='background-color: #f8f9fa; padding: 1rem; border-radius: 0.5rem; margin-bottom: 2rem;'>
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<h4>Welcome to the Medical AI Assistant</h4>
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<p>This AI-powered system helps medical professionals with diagnosis and treatment recommendations.
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Please enter the patient's symptoms and medical history below.</p>
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</div>
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""", unsafe_allow_html=True)
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# Create tabs for different sections
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tab1, tab2 = st.tabs(["π Patient Assessment", "π Results"])
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with tab1:
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col1, col2 = st.columns(2)
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with col1:
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st.subheader("Current Symptoms")
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symptoms = st.text_area(
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'Describe the symptoms in detail',
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placeholder='e.g., persistent fever for 3 days, dry cough, fatigue',
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height=200
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)
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with col2:
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st.subheader("Medical History")
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medical_history = st.text_area(
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'Enter relevant medical history',
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placeholder='e.g., Type 2 diabetes diagnosed in 2019, hypertension',
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height=200
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)
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# Additional Information
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with st.expander("Additional Information (Optional)"):
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col3, col4 = st.columns(2)
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with col3:
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allergies = st.text_area("Known Allergies", placeholder="e.g., penicillin, peanuts")
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current_medications = st.text_area("Current Medications", placeholder="e.g., metformin 500mg twice daily")
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with col4:
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family_history = st.text_area("Family History", placeholder="e.g., heart disease, diabetes")
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lifestyle = st.text_area("Lifestyle Factors", placeholder="e.g., smoker, exercises 3 times a week")
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# Initialize Tools and Agents
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search_tool = SerperDevTool()
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scrape_tool = ScrapeWebsiteTool()
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diagnostician = Agent(
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role="Medical Diagnostician",
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goal="Analyze patient symptoms and medical history to provide a preliminary diagnosis.",
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backstory="Expert in diagnosing medical conditions using advanced algorithms and comprehensive medical knowledge.",
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verbose=True,
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allow_delegation=False,
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tools=[search_tool, scrape_tool],
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treatment_advisor = Agent(
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role="Treatment Advisor",
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goal="Recommend appropriate treatment plans based on the diagnosis.",
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backstory="Specialist in creating personalized treatment plans considering patient history and current medical best practices.",
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verbose=True,
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allow_delegation=False,
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tools=[search_tool, scrape_tool],
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# Define Tasks
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diagnose_task = Task(
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description=(
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f"1. Analyze the patient's symptoms ({symptoms}) and medical history ({medical_history}).\n"
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f"2. Consider additional factors: Age: {age}, Gender: {gender}\n"
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"3. Provide a preliminary diagnosis with possible conditions.\n"
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"4. List the most likely conditions in order of probability."
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),
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expected_output="A detailed preliminary diagnosis with ranked possible conditions.",
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agent=diagnostician
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)
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treatment_task = Task(
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description=(
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"1. Based on the diagnosis, create a comprehensive treatment plan.\n"
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f"2. Consider patient profile: Age: {age}, Gender: {gender}\n"
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f"3. Account for medical history: {medical_history}\n"
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"4. Provide detailed recommendations including:\n"
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" - Medications and dosages\n"
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" - Lifestyle modifications\n"
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" - Follow-up care schedule\n"
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" - Warning signs to watch for"
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),
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expected_output="A comprehensive, personalized treatment plan.",
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agent=treatment_advisor
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)
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# Create Crew
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crew = Crew(
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agents=[diagnostician, treatment_advisor],
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tasks=[diagnose_task, treatment_task],
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verbose=True
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)
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# Analysis Button
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if st.button("Generate Analysis"):
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if not symptoms or not medical_history:
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st.error("Please provide both symptoms and medical history before generating analysis.")
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else:
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with tab2:
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with st.status("π Processing..."):
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st.write("Analyzing patient data...")
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st.write("Generating diagnosis...")
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st.write("Creating treatment plan...")
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result = crew.kickoff(inputs={
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"symptoms": symptoms,
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"medical_history": medical_history
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})
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st.success("Analysis Complete!")
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# Display Results
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st.markdown("### π Analysis Results")
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st.markdown(result)
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# Generate and offer download
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docx_file = generate_docx(result)
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download_link = get_download_link(docx_file, "medical_analysis_report.docx")
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st.markdown("### π₯ Download Report")
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st.markdown(download_link, unsafe_allow_html=True)
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# Additional recommendations
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st.markdown("### β‘ Next Steps")
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st.info("""
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1. Review the generated report in detail
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2. Consider additional specialist consultations if needed
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3. Schedule necessary follow-up appointments
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4. Monitor patient progress and adjust treatment as needed
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""")
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