app.py

import streamlit as st
from streamlit_option_menu import option_menu
import google.generativeai as genai
from PIL import Image as PILImage
import io
import os
import requests
from bs4 import BeautifulSoup
import feedparser
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from reportlab.lib.pagesizes import letter
from reportlab.lib import colors
from reportlab.lib.units import inch
from reportlab.platypus import SimpleDocTemplate, Paragraph, Image, Spacer
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.pdfbase.ttfonts import TTFont
from reportlab.pdfbase import pdfmetrics

# Configure the page
st.set_page_config(
    page_title="MEDUSA AI", 
    page_icon="⚕️", 
    layout="wide", 
    initial_sidebar_state="expanded", 
)

# Custom CSS for background image and styling
st.markdown(
    """
    <style>
    body {
        background-color: #eae7dc;
        background-size: 1200px 800px;
        background-position: center;
        font-family: 'Arial', sans-serif;
    }
    .stApp {
        padding: 15px;
        border-radius: 5px;
        background-color: #eae7dc;
        background-position: center;
    }
    .stButton>button {
        background-color: #116466;
        color: white;
        border: none;
        padding: 10px 20px;
        font-size: 16px;
        border-radius: 5px;
    }
    .stButton>button:hover {
        background-color: #d1e8e2
        color: #5c2018;
    }
    .stTextInput>div>div>input {
        border-radius: 5px;
        border: 1px solid #ccc;
        padding: 10px;
        font-size: 18px;
    }
    .stTextInput>div {
        display: flex;
        justify-content: center;
        margin-top: 80px; /* Adjust the value to move the input box downwards */
    }
    .stSidebar > div {
        background-color: rgba(255, 255, 255, 0.9);
        padding: 15px;
        border-radius: 5px;
    }
    .sidebar-emoji {
        text-align: center;
    }
    .sidebar-emoji img {
        width: 2in;
        height: 2in;
    }
    .chat-message {
        font-size: 18px;
        font-weight: bold;
        color: white;
    }
    </style>
    """,
    unsafe_allow_html=True
)

# MEDUSA GIF
st.sidebar.markdown(
    '<div class="sidebar-emoji"><img src="https://media0.giphy.com/media/dXEP7pHwmGRgNa0Qhu/giphy.webp?cid=ecf05e47l6hasy2f95aa1jzoxvem3hxtylwdrhjuusu48ptj&ep=v1_gifs_search&rid=giphy.webp&ct=s" width="256" height="256" alt="MEDUSA GIF"></div>',
    unsafe_allow_html=True
)

# Navigation menu
selected = option_menu(
    menu_title="Medical Diagnostic Unified System Assistant", 
    options=["Medical Imaging Diagnostics", "Medical Transcription", "Medical Pathology Diagnostics", "Medical Coding", "Insurance Risk Analysis", "Treatment and Diet Plan Generator"],
    icons=["activity", "file-text", "file-medical", "file-code", "shield", "stethoscope"], 
    orientation="horizontal",
    styles={
        "container": {"padding": "0!important", "background-color": "#d8c3a5"},
        "icon": {"color": "#5c2018", "font-size": "15px"}, 
        "nav-link": {"font-size": "15px", "font-family": "serif", "text-align": "center", "margin":"0px", "--hover-color": "#d1e8e2"},
        "nav-link-selected": {"background-color": "#116466"},}
)

# Function to load the Gemini Pro Vision model
@st.cache_resource
def load_model(api_key):
    genai.configure(api_key=api_key)
    return genai.GenerativeModel('gemini-1.5-flash')

# Function to analyze image
def analyze_image(image, prompt, api_key):
    model = load_model(api_key)
    response = model.generate_content([prompt, image])
    return response.text

# Function to fetch and parse RSS feed
def fetch_rss_feed(feed_url):
    feed = feedparser.parse(feed_url)
    if feed.bozo:
        st.error("Failed to fetch RSS feed.")
        return []
    articles = [{'title': entry.title, 'link': entry.link, 'published': entry.get('published', 'No publication date')} for entry in feed.entries]
    return articles

# Function to create a pathology report with matplotlib
def create_pathology_report(patient_info, service_info, specimens, theranostic_report):
    fig, ax = plt.subplots(figsize=(10, 12))

    # Function to add a rectangle with text inside
    def add_textbox(ax, x, y, width, height, header, text, wrap_text=True, fontsize=9, fontweight='normal', ha='left', va='top', line_height=0.02, color='white'):
        rect = patches.Rectangle((x, y), width, height, linewidth=1.5, edgecolor='black', facecolor=color)
        ax.add_patch(rect)
        plt.text(x + 0.01, y + height - 0.01, header, ha=ha, va=va, fontsize=fontsize, fontweight='bold', family='DejaVu Sans')
        
        if wrap_text:
            words = text.split()
            lines = []
            current_line = ""

            for word in words:
                if len(current_line + word) * 0.01 > width:
                    lines.append(current_line)
                    current_line = word + " "
                else:
                    current_line += word + " "

            if current_line:
                lines.append(current_line)

            for i, line in enumerate(lines):
                if i * line_height < height - line_height:
                    plt.text(x + 0.01, y + height - 0.03 - i * line_height, line, ha=ha, va=va, fontsize=fontsize, fontweight=fontweight, family='DejaVu Sans', clip_on=True)
        else:
            plt.text(x + 0.01, y + height - 0.03, text, ha=ha, va=va, fontsize=fontsize, fontweight=fontweight, family='DejaVu Sans', clip_on=True)

    # Add the main header
    plt.text(0.5, 0.96, 'LABORATORY MEDICINE PROGRAM', ha='center', va='center', fontsize=15, family='DejaVu Sans', fontweight='bold')

    # Add the subheader
    plt.text(0.5, 0.93, 'Surgical Pathology Consultation Report', ha='center', va='center', fontsize=13, family='DejaVu Sans', fontweight='bold')

    # Define the increased height for each section
    section_height = 0.8 / 4  # Increased height

    # Add Patient Information box without wrapping text
    add_textbox(ax, 0.05, 0.88 - section_height, 0.9, section_height, 'Patient Information', patient_info, wrap_text=False, fontsize=10, line_height=0.025, color='#E6F2FF')

    # Add Service Information box with wrapping text
    add_textbox(ax, 0.05, 0.88 - 2*section_height, 0.9, section_height, 'Observation', service_info, wrap_text=True, fontsize=10, line_height=0.025, color='#F5F5F5')

    # Add Specimen(s) Received box with wrapping text
    add_textbox(ax, 0.05, 0.88 - 3*section_height, 0.9, section_height, 'Inferences', specimens, wrap_text=True, fontsize=10, line_height=0.025, color='#E6F2FF')

    # Add Consolidated Theranostic Report section with wrapping text
    add_textbox(ax, 0.05, 0.88 - 4*section_height, 0.9, section_height, 'Conclusion', theranostic_report, wrap_text=True, fontsize=10, line_height=0.025, color='#F5F5F5')

    # Add footer information
    plt.text(0.95, 0.01, 'Page 1 of 5', ha='right', va='center', fontsize=10, family='DejaVu Sans')

    # Set the axis limits and hide the axes
    ax.set_xlim(0, 1)
    ax.set_ylim(0, 1)
    ax.axis('off')

    # Save the plot to a buffer
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    plt.close(fig)
    return buf

# Function to create a PDF report
def create_pdf_report(patient_info, service_info, specimens, theranostic_report, diagnosis, detailed_diagnosis, image_buffer, report_format):
    buffer = io.BytesIO()
    doc = SimpleDocTemplate(buffer, pagesize=letter)
    elements = []

    # Set styles and register a custom font
    # pdfmetrics.registerFont(TTFont('Arial', 'arial.ttf'))
    styles = getSampleStyleSheet()
    styleN = ParagraphStyle('Normal', fontName='Helvetica', fontSize=10, leading=12)
    styleH = ParagraphStyle('Heading1', fontName='Helvetica-Bold', fontSize=20, leading=20, alignment=1, spaceAfter=12, underline=True)
    styleH2 = ParagraphStyle('Heading2', fontName='Helvetica-Bold', fontSize=14, leading=14, spaceAfter=8)

    # Different report formats with different background colors
    def add_background_and_border(canvas, doc, background_color):
        canvas.saveState()
        margin = 36
        canvas.setFillColor(background_color)
        canvas.rect(margin, margin, doc.pagesize[0] - 2 * margin, doc.pagesize[1] - 2 * margin, fill=1)
        canvas.setStrokeColor(colors.black)
        canvas.setLineWidth(2)
        canvas.rect(margin, margin, doc.pagesize[0] - 2 * margin, doc.pagesize[1] - 2 * margin)
        canvas.restoreState()

    format_details = {
        "Format 1": {"color": colors.lightblue, "header": "SWAYAM IMAGING CENTER"},
        "Format 2": {"color": colors.lightgreen, "header": "SWAYAM IMAGING CENTER"},
        "Format 3": {"color": colors.lightyellow, "header": "Medical Imaging Report"},
        "Format 4": {"color": colors.lightpink, "header": "IMAGING DIAGNOSTIC CENTER"},
        "Format 5": {"color": colors.lightgrey, "header": "RADIOLOGY REPORT"}
    }

    format_detail = format_details[report_format]
    elements.append(Paragraph(format_detail["header"], styleH))
    elements.append(Spacer(1, 12))
    elements.extend([
        Paragraph(f"Patient Information: {patient_info}", styleN),
        Paragraph(f"Observation: {service_info}", styleN),
        Paragraph(f"Inferences: {specimens}", styleN),
        Spacer(1, 12),
        Paragraph("DIAGNOSIS", styleH2),
        Paragraph(detailed_diagnosis, styleN),
        Spacer(1, 12),
        Paragraph("Conclusion:", styleH2),
        Paragraph(theranostic_report, styleN),
        Spacer(1, 12),
        Paragraph("X-Ray Image:", styleH2),
        Image(image_buffer, width=5 * inch, height=3.5 * inch),
        Spacer(1, 12),
        Paragraph("IMPRESSION", styleH2),
        Paragraph(diagnosis, styleN),
        Spacer(1, 12),
        Paragraph("ADVICE", styleH2),
        Paragraph("Clinical correlation.", styleN),
        Spacer(1, 12),
        Paragraph("Radiologic Technologists: MSC, PGDM", styleN),
        Paragraph("Dr. Payal Shah (MD, Radiologist)", styleN),
        Paragraph("Dr. Vimal Shah (MD, Radiologist)", styleN)
    ])

    doc.build(elements, onFirstPage=lambda canvas, doc: add_background_and_border(canvas, doc, format_detail["color"]), onLaterPages=lambda canvas, doc: add_background_and_border(canvas, doc, format_detail["color"]))
    buffer.seek(0)
    return buffer

# Function to display common instructions
def display_instructions(page):
    st.sidebar.header("Instructions")
    instructions = {
        "Medical Imaging Diagnostics": """
            1. Enter your Google API key in the provided text box.
            2. Upload one or more medical images using the file uploader.
            3. Enter your prompt or use the default one provided.
            4. Click 'Analyze Image' to get the analysis.
            5. If not satisfied with the analysis, click 'Regenerate Analysis'.
            6. View related research papers based on the analysis.
        """,
        "Medical Transcription": """
            1. Enter your Google API key in the provided text box.
            2. Upload a medical prescription image using the file uploader.
            3. Enter your prompt or use the default one provided.
            4. Click 'Get Transcription' to see the analysis in tabular format.
        """,
        "Medical Pathology Diagnostics": """
            1. Enter your Google API key in the provided text box.
            2. Upload a medical report image using the file uploader.
            3. Enter your prompt or use the default one provided.
            4. Click 'Analyze Report' to get the analysis and generate the pathology report.
        """,
        "Medical Coding": """
            1. Enter your Google API key in the provided text box.
            2. Upload a medical document image using the file uploader.
            3. Enter your prompt or use the default one provided.
            4. Click 'Get ICD Codes' to see the suggested ICD medical codes with descriptions.
        """,
        "Insurance Risk Analysis": """
            1. Enter your Google API key in the provided text box.
            2. Upload an image containing user data using the file uploader.
            3. Enter your prompt or use the default one provided.
            4. Click 'Analyze Risk' to get the percentage risk and detailed justification.
        """,
        "Treatment and Diet Plan Generator": """
            1. Enter your Google API key in the provided text box.
            2. Upload an image containing patient data using the file uploader.
            3. Enter your prompt or use the default one provided.
            4. Click 'Generate Plan' to get the treatment and diet plans.
        """
    }
    st.sidebar.markdown(instructions.get(page, ""))

# Function to display medical news
def display_medical_news():
    st.sidebar.header("📰 Latest Medical News")
    show_news_button = st.sidebar.button("Show Medical News")
    if show_news_button:
        feed_url = "https://health.economictimes.indiatimes.com/rss/topstories"
        articles = fetch_rss_feed(feed_url)
        if articles:
            for article in articles:
                st.sidebar.markdown(f"<div style='font-size: 0.9rem;'><b>Title:</b> <a href='{article['link']}'>{article['title']}</a><br><b>Published:</b> {article['published']}</div>", unsafe_allow_html=True)
        else:
            st.sidebar.info("No articles available at the moment.")

# Function to handle Medical Imaging Diagnostics section
def medical_imaging_diagnostics():
    st.header("Medical Imaging Diagnostics")

    st.header("Upload Image")
    uploaded_files = st.file_uploader("Choose medical images...", type=["jpg", "jpeg", "png"], accept_multiple_files=True)

    st.header("API Key")
    api_key = st.text_input("Enter your Google API key:", type="password")

    default_prompt = "Analyze this medical image. Describe what you see, identify any abnormalities, and suggest potential diagnoses."
    prompt = default_prompt

    analyze_button = st.button("Analyze Image")
    regenerate_button = st.button("Regenerate Analysis")

    st.header("Report Format")
    report_format = st.selectbox("Choose Report Format:", ["Format 1", "Format 2", "Format 3", "Format 4", "Format 5"])

    if uploaded_files and api_key:
        for uploaded_file in uploaded_files:
            col1, col2 = st.columns(2)

            with col1:
                st.header("Uploaded Image")
                image = PILImage.open(uploaded_file)
                st.image(image, caption="Uploaded Medical Image", use_column_width=True)

            with col2:
                st.header("Image Analysis")
                if analyze_button or regenerate_button:
                    with st.spinner("Analyzing the image..."):
                        try:
                            analysis = analyze_image(image, prompt, api_key)
                            st.markdown(analysis)

                            # Extract the diagnosis from the analysis
                            detailed_diagnosis = analysis
                            diagnosis = analysis.split('.')[0]

                            # Save the uploaded image to a buffer
                            img_buffer = io.BytesIO()
                            image.save(img_buffer, format='PNG')
                            img_buffer.seek(0)

                            # Generate PDF report
                            pdf_buffer = create_pdf_report("Yashvi M. Patel", 21, "Female", diagnosis, detailed_diagnosis, "", img_buffer, report_format)
                            st.download_button(label="Download Report", data=pdf_buffer, file_name="medical_report.pdf", mime="application/pdf")
                            
                        except Exception as e:
                            st.error(f"An error occurred: {str(e)}")
                else:
                    st.info("Click 'Analyze Image' to start the analysis.")
    elif not api_key:
        st.warning("Please enter your Google API key.")

# Function to handle Medical Transcription section
def medical_transcription():
    st.header("Medical Transcription")

    st.header("Upload Prescription")
    uploaded_file = st.file_uploader("Choose a medical prescription image...", type=["jpg", "jpeg", "png"])

    st.header("API Key")
    api_key = st.text_input("Enter your Google API key:", type="password")

    default_prompt = "Analyze this medical prescription and transcribe it in tabular format."
    prompt = default_prompt
    
    analyze_button = st.button("Get Transcription")

    col1, col2 = st.columns(2)

    with col1:
        st.header("Uploaded Prescription")
        if uploaded_file is not None:
            image = PILImage.open(uploaded_file)
            st.image(image, caption="Uploaded Prescription", use_column_width=True)
        else:
            st.info("Please upload an image using the uploader.")

    with col2:
        st.header("Transcription in Tabular Format")
        if uploaded_file is not None and analyze_button and api_key:
            with st.spinner("Analyzing the image..."):
                try:
                    image = PILImage.open(uploaded_file)
                    analysis = analyze_image(image, prompt, api_key)
                    st.markdown(analysis)
                except Exception as e:
                    st.error(f"An error occurred: {str(e)}")
        elif uploaded_file is None:
            st.info("Upload an image and click 'Get Transcription' to see the results.")
        elif not analyze_button:
            st.info("Click 'Get Transcription' to start the analysis.")
        elif not api_key:
            st.warning("Please enter your Google API key.")

# Function to extract patient info, service info, and specimens from the analysis
def extract_info_from_analysis(analysis):
    theranostic_report = """lorem ipsum
    lorem ipsum
    lorem ipsum"""
    patient_info = "Patient Name:         N.A.\n" \
                   "MRN:                          N.A.\n" \
                   "DOB:                          N.A. (Age: N.A.)\n" \
                   "Gender:                      N.A.\n" \
                   "HCN:                          N.A.\n" \
                   "Ordering MD:            N.A.\n" \
                   "Copy To:                   N.A.\n" \
                   "                                      N.A."

    service_info = """lorem ipsum
    lorem ipsum
    lorem ipsum"""

    specimens = """lorem ipsum
    lorem ipsum
    lorem ipsum"""

    # Example parsing logic (this should be customized to the format of the analysis text)
    if "Patient Name:" in analysis:
        patient_info = analysis.split("Patient Name:")[1].split("Observation:")[0].strip()
    if "Observation:" in analysis:
        service_info = analysis.split("Observation:")[1].split("Inferences:")[0].strip()
    if "Inferences:" in analysis:
        specimens= analysis.split("Inferences:")[1].split("Conclusion:")[0].strip()    
    if "Conclusion:" in analysis:
        theranostic_report = analysis.split("Conclusion:")[1].strip()     

    return patient_info, service_info, specimens, theranostic_report

# Function to handle Medical Pathology Diagnostics section
def medical_pathology_diagnostics():
    st.header("Medical Pathology Diagnostics")

    st.header("Upload Report")
    uploaded_file = st.file_uploader("Choose a medical report image...", type=["jpg", "jpeg", "png"])
    
    st.header("API Key")
    api_key = st.text_input("Enter your Google API key:", type="password")

    default_prompt = """You are a highly skilled medical professional specializing in pathology. Please analyze the uploaded medical pathology report and extract the following information accurately and concisely. Present the information in a structured format with clear labels:

1. **Patient Information:**
   - Patient Name
   - Medical Record Number (MRN)
   - Date of Birth (DOB) with Age
   - Gender
   - Health Card Number (HCN)
   - Ordering Physician
   - Copy To (if any)

2. **Observation:**
   - Summarize the key observations noted in the report in a short paragraph.

3. **Inferences:**
   - Summarize the main inferences derived from the observations in a short paragraph.

4. **Conclusion:**
   - Provide the final conclusion or diagnosis mentioned in the report in a short paragraph.

**Format for Output:**

- **Patient Information:**
  - Patient Name: [Extracted Name]
  - MRN: [Extracted MRN]
  - DOB: [Extracted DOB] (Age: [Extracted Age])
  - Gender: [Extracted Gender]
  - HCN: [Extracted HCN]
  - Ordering Physician: [Extracted Physician]
  - Copy To: [Extracted Copy To (if any)]

- **Observation:**
  - [Summarized Observations]

- **Inferences:**
  - [Summarized Inferences]

- **Conclusion:**
  - [Final Conclusion or Diagnosis]

Ensure that the extracted information is accurate and formatted correctly.


"""

    prompt = default_prompt
    
    analyze_button = st.button("Analyze Report")

    col1, col2 = st.columns(2)

    with col1:
        st.header("Uploaded Report")
        if uploaded_file is not None:
            image = PILImage.open(uploaded_file)
            st.image(image, caption="Uploaded Medical Report", use_column_width=True)
        else:
            st.info("Please upload an image using the uploader.")

    with col2:
        st.header("Report Analysis")
        if uploaded_file is not None and analyze_button and api_key:
            with st.spinner("Analyzing the image..."):
                try:
                    image = PILImage.open(uploaded_file)
                    analysis = analyze_image(image, prompt, api_key)

                    # Extract relevant details for the report
                    patient_info, service_info, specimens, theranostic_report = extract_info_from_analysis(analysis)
                    
                    # Generate pathology report
                    report_buf = create_pathology_report(patient_info, service_info, specimens, theranostic_report)
                    st.image(report_buf, caption="Pathology Report", use_column_width=True)

                    # Save the analysis as image
                    st.download_button(label="Download Report Image", data=report_buf, file_name="pathology_report.png", mime="image/png")
                    
                except Exception as e:
                    st.error(f"An error occurred: {str(e)}")
        elif uploaded_file is None:
            st.info("Upload an image and click 'Analyze Report' to see the results.")
        elif not analyze_button:
            st.info("Click 'Analyze Report' to start the analysis.")
        elif not api_key:
            st.warning("Please enter your Google API key.")

# Function to handle Medical Coding section
def medical_coding():
    st.header("Medical Coding")

    st.header("Upload Medical Document")
    uploaded_file = st.file_uploader("Choose a medical document image...", type=["jpg", "jpeg", "png"])

    st.header("API Key")
    api_key = st.text_input("Enter your Google API key:", type="password")
    
    default_prompt = "Analyze the image and suggest the ICD medical codes with description. Make it simple and concise."
    prompt = default_prompt
    
    analyze_button = st.button("Get ICD Codes")

    col1, col2 = st.columns(2)

    with col1:
        st.header("Uploaded Medical Document")
        if uploaded_file is not None:
            image = PILImage.open(uploaded_file)
            st.image(image, caption="Uploaded Medical Document", use_column_width=True)
        else:
            st.info("Please upload an image using the uploader.")

    with col2:
        st.header("ICD Codes and Descriptions")
        if uploaded_file is not None and analyze_button and api_key:
            with st.spinner("Analyzing the image..."):
                try:
                    image = PILImage.open(uploaded_file)
                    analysis = analyze_image(image, prompt, api_key)
                    st.markdown(analysis)
                except Exception as e:
                    st.error(f"An error occurred: {str(e)}")
        elif uploaded_file is None:
            st.info("Upload an image and click 'Get ICD Codes' to see the results.")
        elif not analyze_button:
            st.info("Click 'Get ICD Codes' to start the analysis.")
        elif not api_key:
            st.warning("Please enter your Google API key.")

# Function to handle Insurance Risk Analysis section
def insurance_risk_analysis():
    st.header("Insurance Risk Analysis")

    st.header("Upload User Data Image")
    uploaded_file = st.file_uploader("Choose an image containing user data...", type=["jpg", "jpeg", "png"])

    st.header("API Key")
    api_key = st.text_input("Enter your Google API key:", type="password")
    
    default_prompt = """You are a highly skilled insurance analyst. Please analyze the uploaded image containing user data and calculate the insurance risk percentage. Provide a detailed justification for the calculated risk percentage based on the data.

**Format for Output:**

- **Risk Percentage:** [Calculated Percentage]%
- **Justification:** [Detailed Justification]

Ensure that the calculated risk and justification are accurate and well-explained."""

    prompt = default_prompt
    
    analyze_button = st.button("Analyze Risk")

    col1, col2 = st.columns(2)

    with col1:
        st.header("Uploaded User Data Image")
        if uploaded_file is not None:
            image = PILImage.open(uploaded_file)
            st.image(image, caption="Uploaded User Data Image", use_column_width=True)
        else:
            st.info("Please upload an image using the uploader.")

    with col2:
        st.header("Risk Analysis")
        if uploaded_file is not None and analyze_button and api_key:
            with st.spinner("Analyzing the image..."):
                try:
                    image = PILImage.open(uploaded_file)
                    analysis = analyze_image(image, prompt, api_key)
                    st.markdown(analysis)
                except Exception as e:
                    st.error(f"An error occurred: {str(e)}")
        elif uploaded_file is None:
            st.info("Upload an image and click 'Analyze Risk' to see the results.")
        elif not analyze_button:
            st.info("Click 'Analyze Risk' to start the analysis.")
        elif not api_key:
            st.warning("Please enter your Google API key.")

# Function to handle Treatment and Diet Plan Generator section
def treatment_diet_plan_generator():
    st.header("Treatment and Diet Plan Generator")

    st.header("Upload Patient Data Image")
    uploaded_file = st.file_uploader("Choose an image containing patient data...", type=["jpg", "jpeg", "png"])

    st.header("API Key")
    api_key = st.text_input("Enter your Google API key:", type="password")

    default_prompt = """You are a highly skilled medical professional. Please analyze the uploaded image containing patient data and generate a treatment plan and a diet plan based on the information provided.

**Format for Output:**

- **Treatment Plan:**
  - [Generated Treatment Plan]

- **Diet Plan:**
  - [Generated Diet Plan]

Ensure that the plans are accurate and well-explained."""

    prompt = default_prompt
    
    generate_plan_button = st.button("Generate Plan")

    col1, col2 = st.columns(2)

    with col1:
        st.header("Uploaded Patient Data Image")
        if uploaded_file is not None:
            image = PILImage.open(uploaded_file)
            st.image(image, caption="Uploaded Patient Data Image", use_column_width=True)
        else:
            st.info("Please upload an image using the uploader.")

    with col2:
        st.header("Treatment and Diet Plan")
        if uploaded_file is not None and generate_plan_button and api_key:
            with st.spinner("Generating plans..."):
                try:
                    image = PILImage.open(uploaded_file)
                    analysis = analyze_image(image, prompt, api_key)
                    st.markdown(analysis)
                except Exception as e:
                    st.error(f"An error occurred: {str(e)}")
        elif uploaded_file is None:
            st.info("Upload an image and click 'Generate Plan' to see the results.")
        elif not generate_plan_button:
            st.info("Click 'Generate Plan' to start the analysis.")
        elif not api_key:
            st.warning("Please enter your Google API key.")

# Main app
def main():
    st.sidebar.markdown("<h3 style='text-align: center; color: #116466; font-family: comic sans ms;'>⚕️ MEDUSA AI</h3>", unsafe_allow_html=True)
    display_instructions(selected)
    display_medical_news()

    if selected == "Medical Imaging Diagnostics":
        medical_imaging_diagnostics()
    elif selected == "Medical Transcription":
        medical_transcription()
    elif selected == "Medical Pathology Diagnostics":
        medical_pathology_diagnostics()
    elif selected == "Medical Coding":
        medical_coding()
    elif selected == "Insurance Risk Analysis":
        insurance_risk_analysis()
    elif selected == "Treatment and Diet Plan Generator":
        treatment_diet_plan_generator()

if __name__ == "__main__":
    main()