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| import streamlit as st | |
| import google.generativeai as genai | |
| from PIL import Image | |
| import os | |
| from dotenv import load_dotenv | |
| import PyPDF2 | |
| import io | |
| from datetime import datetime | |
| # Page configuration | |
| st.set_page_config( | |
| page_title="Cornea AI Pentacam Analyzer", | |
| page_icon="👁️", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # Load environment variables | |
| load_dotenv() | |
| # Configure Gemini API | |
| genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) | |
| model = genai.GenerativeModel("gemini-2.0-flash-exp") | |
| # Custom CSS | |
| st.markdown(""" | |
| <style> | |
| .main { | |
| padding: 2rem; | |
| } | |
| .stButton>button { | |
| width: 100%; | |
| background-color: #2E86C1; | |
| color: white; | |
| padding: 0.5rem; | |
| margin-top: 1rem; | |
| } | |
| .credit-box { | |
| background-color: #f0f2f6; | |
| padding: 1.5rem; | |
| border-radius: 0.5rem; | |
| margin: 1rem 0; | |
| border-left: 5px solid #2E86C1; | |
| } | |
| .header-box { | |
| background: linear-gradient(135deg, #2E86C1, #3498DB); | |
| padding: 2rem; | |
| border-radius: 0.5rem; | |
| color: white; | |
| margin-bottom: 2rem; | |
| text-align: center; | |
| box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| } | |
| .image-container { | |
| margin: 1rem 0; | |
| padding: 1rem; | |
| border-radius: 0.5rem; | |
| background-color: #f8f9fa; | |
| border: 1px solid #e9ecef; | |
| } | |
| .analysis-container { | |
| margin-top: 1rem; | |
| padding: 1.5rem; | |
| border-radius: 0.5rem; | |
| background-color: #f8f9fa; | |
| border: 1px solid #e9ecef; | |
| } | |
| .patient-info { | |
| background-color: #fff; | |
| padding: 1.5rem; | |
| border-radius: 0.5rem; | |
| border: 1px solid #e9ecef; | |
| margin-bottom: 1rem; | |
| } | |
| .upload-section { | |
| background-color: #f8f9fa; | |
| padding: 1.5rem; | |
| border-radius: 0.5rem; | |
| border: 1px dashed #2E86C1; | |
| margin: 1rem 0; | |
| } | |
| .info-box { | |
| background-color: #e1f5fe; | |
| padding: 1rem; | |
| border-radius: 0.5rem; | |
| margin: 0.5rem 0; | |
| border-left: 3px solid #03a9f4; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # System prompts | |
| CORNEA_ANALYSIS_PROMPT = """You are an expert ophthalmologist specializing in corneal diseases. A user will ask you questions about interpreting corneal imaging, specifically Pentacam, and about subclinical corneal edema. | |
| **Part 1: Pentacam Interpretation** | |
| • Analyze Pentacam imaging for diagnosing and monitoring corneal diseases | |
| • Assess key parameters: corneal shape, thickness, and elevation | |
| • Evaluate anterior and posterior corneal surfaces | |
| • Consider ABCD staging system and Cruces University Hospital progression score | |
| **Part 2: Subclinical Corneal Edema Analysis** | |
| • Evaluate for Fuchs Endothelial Corneal Dystrophy (FECD) | |
| • Apply Sun criteria for subclinical corneal edema | |
| • Assess displacement of thinnest point | |
| • Check for loss of parallel isopachs | |
| • Evaluate focal depression of posterior surface | |
| Please analyze the provided images and provide a detailed assessment.""" | |
| COMPARISON_PROMPT = """Compare the provided Pentacam scans from different timepoints, focusing on: | |
| 1. Changes in corneal parameters: | |
| • Thickness variations | |
| • Topographic changes | |
| • Elevation differences | |
| • Progression indicators | |
| 2. Clinical significance: | |
| • Disease progression/stability | |
| • Treatment response | |
| • Risk assessment | |
| 3. Recommendations: | |
| • Follow-up interval | |
| • Treatment modifications | |
| • Additional testing needs""" | |
| def extract_pdf_text(pdf_file): | |
| """Extract text from uploaded PDF file""" | |
| pdf_reader = PyPDF2.PdfReader(pdf_file) | |
| text = "" | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| return text | |
| def analyze_cornea_images(images, timepoint=None, patient_data=None): | |
| """Analyze corneal images with optional timepoint and patient data""" | |
| if timepoint: | |
| prompt = f"{CORNEA_ANALYSIS_PROMPT}\n\nTimepoint: {timepoint}\n" | |
| else: | |
| prompt = f"{CORNEA_ANALYSIS_PROMPT}\n" | |
| if patient_data: | |
| prompt += f"\nPatient Information:\n{patient_data}\n" | |
| prompt += "\nPlease analyze these corneal scans:" | |
| content = [prompt] + images | |
| response = model.generate_content(content) | |
| return response.text | |
| def compare_timepoints(images1, date1, images2, date2, patient_data=None): | |
| """Compare corneal images from two timepoints""" | |
| prompt = f"{COMPARISON_PROMPT}\n\nTimepoint 1: {date1}\nTimepoint 2: {date2}\n" | |
| if patient_data: | |
| prompt += f"\nPatient Information:\n{patient_data}\n" | |
| prompt += "\nPlease compare these corneal scans:" | |
| content = [prompt] + images1 + images2 | |
| response = model.generate_content(content) | |
| return response.text | |
| def main(): | |
| # Header | |
| st.markdown(""" | |
| <div class="header-box"> | |
| <h1>Cornea AI Pentacam Analyzer</h1> | |
| <p style="font-size: 1.2em; margin-top: 1rem;">Advanced Corneal Analysis & Diagnostics</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Credits | |
| st.markdown(""" | |
| <div class="credit-box"> | |
| <h3>About</h3> | |
| <p>Developed by Dr. Verónica Gómez Calleja</p> | |
| <p>Cornea Specialist</p> | |
| <p>This advanced tool assists in the analysis of Pentacam scans and corneal conditions using state-of-the-art AI technology. | |
| It provides comprehensive analysis of corneal parameters and supports clinical decision-making in keratoconus, FECD, and other corneal conditions.</p> | |
| <p><strong>Note:</strong> This tool is for assistance only and should not replace professional medical judgment.</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Main content | |
| col1, col2 = st.columns([1, 1]) | |
| with col1: | |
| # Patient Information Section | |
| st.markdown("### Patient Information") | |
| with st.container(): | |
| st.markdown('<div class="patient-info">', unsafe_allow_html=True) | |
| patient_name = st.text_input("Patient Name") | |
| patient_id = st.text_input("Patient ID") | |
| dob = st.date_input("Date of Birth") | |
| diagnosis = st.text_input("Previous Diagnosis (if any)") | |
| # Clinical History | |
| st.markdown("#### Clinical History") | |
| history_text = st.text_area("Enter relevant clinical history") | |
| # Multiple PDF uploads for patient records | |
| st.markdown("#### Additional Patient Records (PDF)") | |
| patient_pdfs = st.file_uploader("Upload Patient Records", type=['pdf'], accept_multiple_files=True) | |
| if patient_pdfs: | |
| for pdf in patient_pdfs: | |
| with st.expander(f"View {pdf.name}"): | |
| st.text(extract_pdf_text(pdf)) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| # Pentacam Scan Upload Section | |
| st.markdown("### Upload Pentacam Scans") | |
| st.markdown('<div class="upload-section">', unsafe_allow_html=True) | |
| scan_type = st.radio("Select Analysis Type", ["Single Timepoint", "Compare Timepoints"]) | |
| if scan_type == "Single Timepoint": | |
| scan_date = st.date_input("Scan Date") | |
| uploaded_files = st.file_uploader("Upload Pentacam Scans", type=['png', 'jpg', 'jpeg', 'pdf'], accept_multiple_files=True) | |
| if uploaded_files: | |
| images = [] | |
| for file in uploaded_files: | |
| if file.type.startswith('image'): | |
| image = Image.open(file) | |
| images.append(image) | |
| elif file.type == 'application/pdf': | |
| # Handle PDF files here if needed | |
| st.warning(f"PDF processing for {file.name} will be implemented soon") | |
| if images: | |
| st.markdown("#### Preview Uploaded Scans") | |
| cols = st.columns(len(images)) | |
| for idx, (col, img) in enumerate(zip(cols, images)): | |
| with col: | |
| st.image(img, caption=f"Scan {idx + 1}", use_column_width=True) | |
| if st.button("Analyze Scans"): | |
| with st.spinner("Analyzing..."): | |
| analysis = analyze_cornea_images( | |
| images, | |
| timepoint=scan_date.strftime("%Y-%m-%d"), | |
| patient_data=f"Name: {patient_name}\nID: {patient_id}\nDOB: {str(dob)}\nDiagnosis: {diagnosis}\nHistory: {history_text}" | |
| ) | |
| st.markdown("### Analysis Results") | |
| st.markdown('<div class="analysis-container">', unsafe_allow_html=True) | |
| st.markdown(analysis) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| else: # Compare Timepoints | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown("#### First Timepoint") | |
| date1 = st.date_input("Date of First Scan") | |
| files1 = st.file_uploader("Upload First Scans", type=['png', 'jpg', 'jpeg', 'pdf'], accept_multiple_files=True) | |
| with col2: | |
| st.markdown("#### Second Timepoint") | |
| date2 = st.date_input("Date of Second Scan") | |
| files2 = st.file_uploader("Upload Second Scans", type=['png', 'jpg', 'jpeg', 'pdf'], accept_multiple_files=True) | |
| if files1 and files2: | |
| images1 = [Image.open(f) for f in files1 if f.type.startswith('image')] | |
| images2 = [Image.open(f) for f in files2 if f.type.startswith('image')] | |
| if images1 and images2: | |
| st.markdown("#### Preview First Timepoint") | |
| cols1 = st.columns(len(images1)) | |
| for idx, (col, img) in enumerate(zip(cols1, images1)): | |
| with col: | |
| st.image(img, caption=f"Scan {idx + 1}", use_column_width=True) | |
| st.markdown("#### Preview Second Timepoint") | |
| cols2 = st.columns(len(images2)) | |
| for idx, (col, img) in enumerate(zip(cols2, images2)): | |
| with col: | |
| st.image(img, caption=f"Scan {idx + 1}", use_column_width=True) | |
| if st.button("Compare Scans"): | |
| with st.spinner("Analyzing..."): | |
| comparison = compare_timepoints( | |
| images1, date1.strftime("%Y-%m-%d"), | |
| images2, date2.strftime("%Y-%m-%d"), | |
| patient_data=f"Name: {patient_name}\nID: {patient_id}\nDOB: {str(dob)}\nDiagnosis: {diagnosis}\nHistory: {history_text}" | |
| ) | |
| st.markdown("### Comparison Results") | |
| st.markdown('<div class="analysis-container">', unsafe_allow_html=True) | |
| st.markdown(comparison) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| main() |