Enhanced app with multi-timepoint progression analysis and simplified patient data input

app.py

@@ -6,6 +6,8 @@ from dotenv import load_dotenv
 import PyPDF2
 import io
 from datetime import datetime
+import pandas as pd
+from collections import defaultdict
 
 # Page configuration
 st.set_page_config(
@@ -86,60 +88,89 @@ st.markdown("""
         margin: 0.5rem 0;
         border-left: 3px solid #03a9f4;
     }
+    .timeline-container {
+        margin: 2rem 0;
+        padding: 1rem;
+        background-color: #fff;
+        border-radius: 0.5rem;
+        border: 1px solid #e9ecef;
+    }
+    .timepoint-card {
+        background-color: #f8f9fa;
+        padding: 1rem;
+        margin: 0.5rem 0;
+        border-radius: 0.5rem;
+        border-left: 3px solid #2E86C1;
+    }
     </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.
+CORNEA_ANALYSIS_PROMPT = """You are an expert ophthalmologist specializing in corneal diseases. Analyze these Pentacam scans and patient data with focus on:
 
-**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
+1. Corneal Parameters Analysis:
+   • Thickness mapping and progression
+   • Topographic changes
+   • Elevation data (anterior and posterior)
+   • Keratoconus indices and classification
 
-**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
+2. Disease Assessment:
+   • ABCD Keratoconus staging
+   • Fuchs Endothelial Corneal Dystrophy evaluation
+   • Subclinical corneal edema (Sun criteria)
+   • Risk assessment
 
-Please analyze the provided images and provide a detailed assessment."""
+3. Clinical Interpretation:
+   • Pattern recognition
+   • Disease progression markers
+   • Treatment implications
 
-COMPARISON_PROMPT = """Compare the provided Pentacam scans from different timepoints, focusing on:
+Please provide a detailed clinical assessment."""
 
-1. Changes in corneal parameters:
-   • Thickness variations
-   • Topographic changes
-   • Elevation differences
-   • Progression indicators
+PROGRESSION_ANALYSIS_PROMPT = """Analyze the progression of corneal parameters across multiple timepoints, focusing on:
 
-2. Clinical significance:
-   • Disease progression/stability
-   • Treatment response
-   • Risk assessment
+1. Temporal Changes:
+   • Progressive changes in corneal thickness
+   • Evolution of topographic patterns
+   • Changes in elevation maps
+   • Progression of keratoconus indices
+
+2. Rate of Progression:
+   • Quantify changes between timepoints
+   • Identify acceleration or stabilization periods
+   • Compare with expected disease progression
+
+3. Risk Assessment:
+   • Current status evaluation
+   • Future progression risk
+   • Treatment recommendations
 
-3. Recommendations:
-   • Follow-up interval
-   • Treatment modifications
-   • Additional testing needs"""
+4. Timeline Analysis:
+   • Key changes between each timepoint
+   • Overall progression pattern
+   • Critical periods of change
 
-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
+Please provide a comprehensive progression analysis with clinical recommendations."""
 
-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"
+def extract_patient_data(uploaded_file):
+    """Extract and process patient data from uploaded file"""
+    patient_data = {}
+    
+    if uploaded_file.type == "application/pdf":
+        pdf_reader = PyPDF2.PdfReader(uploaded_file)
+        text = ""
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+        patient_data['raw_text'] = text
     else:
-        prompt = f"{CORNEA_ANALYSIS_PROMPT}\n"
+        # Handle other file types if needed
+        patient_data['raw_text'] = "File type not supported for detailed extraction"
     
+    return patient_data
+
+def analyze_timepoint(images, date, patient_data=None):
+    """Analyze a single timepoint"""
+    prompt = f"{CORNEA_ANALYSIS_PROMPT}\n\nTimepoint: {date}\n"
     if patient_data:
         prompt += f"\nPatient Information:\n{patient_data}\n"
     
@@ -148,15 +179,19 @@ def analyze_cornea_images(images, timepoint=None, patient_data=None):
     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"
+def analyze_progression(timepoints_data):
+    """Analyze progression across multiple timepoints"""
+    prompt = f"{PROGRESSION_ANALYSIS_PROMPT}\n\n"
+    prompt += "Timepoints for analysis:\n"
     
-    if patient_data:
-        prompt += f"\nPatient Information:\n{patient_data}\n"
+    # Add all timepoints to the prompt
+    all_images = []
+    for date, images in timepoints_data.items():
+        prompt += f"\n- {date}:"
+        all_images.extend(images)
     
-    prompt += "\nPlease compare these corneal scans:"
-    content = [prompt] + images1 + images2
+    prompt += "\n\nPlease analyze the progression across these timepoints:"
+    content = [prompt] + all_images
     response = model.generate_content(content)
     return response.text
 
@@ -181,111 +216,99 @@ def main():
         </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)
+    # Patient Information Section
+    st.markdown("### Patient Information")
+    patient_file = st.file_uploader("Upload Patient Information (PDF/Text)", type=['pdf', 'txt'])
+    patient_data = None
+    if patient_file:
+        patient_data = extract_patient_data(patient_file)
+        with st.expander("View Extracted Patient Information"):
+            st.text(patient_data.get('raw_text', 'No text extracted'))
 
-        # Pentacam Scan Upload Section
-        st.markdown("### Upload Pentacam Scans")
+    # Scan Analysis Section
+    st.markdown("### Pentacam Scan Analysis")
+    analysis_type = st.radio("Select Analysis Type", ["Single Timepoint", "Multiple Timepoint Progression"])
+
+    if analysis_type == "Single Timepoint":
         st.markdown('<div class="upload-section">', unsafe_allow_html=True)
-        scan_type = st.radio("Select Analysis Type", ["Single Timepoint", "Compare Timepoints"])
+        scan_date = st.date_input("Scan Date")
+        uploaded_files = st.file_uploader("Upload Pentacam Scans", type=['png', 'jpg', 'jpeg'], accept_multiple_files=True)
         
-        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:
+                image = Image.open(file)
+                images.append(image)
             
-            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 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 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)
+                if st.button("Analyze Scans"):
+                    with st.spinner("Analyzing..."):
+                        analysis = analyze_timepoint(
+                            images,
+                            scan_date.strftime("%Y-%m-%d"),
+                            patient_data.get('raw_text') if patient_data else None
+                        )
+                        st.markdown("### Analysis Results")
+                        st.markdown('<div class="analysis-container">', unsafe_allow_html=True)
+                        st.markdown(analysis)
+                        st.markdown('</div>', unsafe_allow_html=True)
+        
+        st.markdown('</div>', unsafe_allow_html=True)
+    
+    else:  # Multiple Timepoint Progression
+        st.markdown('<div class="upload-section">', unsafe_allow_html=True)
+        st.info("Upload scans from multiple timepoints to analyze progression")
+        
+        # Create a dynamic form for multiple timepoints
+        timepoints_data = defaultdict(list)
+        num_timepoints = st.number_input("Number of Timepoints", min_value=2, max_value=10, value=3)
         
-        else:  # Compare Timepoints
-            col1, col2 = st.columns(2)
+        st.markdown('<div class="timeline-container">', unsafe_allow_html=True)
+        for i in range(num_timepoints):
+            st.markdown(f'<div class="timepoint-card">', unsafe_allow_html=True)
+            col1, col2 = st.columns([1, 3])
             
             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)
+                date = st.date_input(f"Date - Timepoint {i+1}", key=f"date_{i}")
             
             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')]
+                files = st.file_uploader(
+                    f"Upload Scans - Timepoint {i+1}",
+                    type=['png', 'jpg', 'jpeg'],
+                    accept_multiple_files=True,
+                    key=f"files_{i}"
+                )
                 
-                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 files:
+                    images = []
+                    for file in files:
+                        image = Image.open(file)
+                        images.append(image)
+                        timepoints_data[date.strftime("%Y-%m-%d")].extend(images)
                     
-                    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)
+                    if images:
+                        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)
+            
+            st.markdown('</div>', unsafe_allow_html=True)
+        st.markdown('</div>', unsafe_allow_html=True)
+        
+        if timepoints_data and len(timepoints_data) >= 2:
+            if st.button("Analyze Progression"):
+                with st.spinner("Analyzing progression across timepoints..."):
+                    progression_analysis = analyze_progression(timepoints_data)
+                    st.markdown("### Progression Analysis Results")
+                    st.markdown('<div class="analysis-container">', unsafe_allow_html=True)
+                    st.markdown(progression_analysis)
+                    st.markdown('</div>', unsafe_allow_html=True)
         
         st.markdown('</div>', unsafe_allow_html=True)