pages/2_PDF_Report.py

"""
PDF Report Generator page for Smartwatch Normative Z-Score Calculator.

Generate downloadable PDF reports for individual patients.
"""
import streamlit as st
import sys
import os

# Add parent directory to path for imports
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from batch_utils import generate_pdf_report, BIOMARKER_LABELS, AVAILABLE_BIOMARKERS, HIGHER_IS_BETTER
import normalizer_model

st.set_page_config(
    page_title="PDF Report - Smartwatch Z-Score Calculator",
    page_icon="📄",
    layout="wide",
)

# Load normative data
DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "Table_1_summary_measure.csv")

@st.cache_data
def get_normative_data():
    try:
        return normalizer_model.load_normative_table(DATA_PATH)
    except Exception as e:
        st.error(f"Could not load normative data: {e}")
        return None

normative_df = get_normative_data()

st.title("📄 PDF Report Generator")
st.markdown("**Generate a professional smartwatch biomarker report for download**")

st.info(
    "Enter patient information and biomarker measurements below to generate a downloadable PDF report "
    "with z-scores, percentiles, and visual gauges."
)

col1, col2 = st.columns(2)

with col1:
    st.subheader("👤 Patient Information")
    
    patient_name = st.text_input(
        "Patient Name/ID (optional)", 
        placeholder="e.g., John Doe or P001"
    )
    
    # Region with default Western Europe
    if normative_df is not None:
        regions = sorted(normative_df["area"].unique())
        if "Western Europe" in regions:
            default_region_idx = regions.index("Western Europe")
        else:
            default_region_idx = 0
    else:
        regions = ["Western Europe", "Southern Europe", "North America", "Japan"]
        default_region_idx = 0
    
    region = st.selectbox(
        "Region",
        regions,
        index=default_region_idx
    )
    
    # Gender
    if normative_df is not None:
        genders = sorted(normative_df["gender"].unique())
    else:
        genders = ["Man", "Woman"]
    
    gender = st.selectbox("Gender", genders)
    
    age = st.number_input(
        "Age (years)",
        min_value=0,
        max_value=120,
        value=45
    )
    
    bmi = st.number_input(
        "BMI",
        min_value=10.0,
        max_value=60.0,
        value=24.0,
        step=0.1,
        format="%.1f"
    )

with col2:
    st.subheader("📊 Biomarker Measurements")
    st.caption("Select which biomarkers to include in the report")
    
    # Biomarker inputs with checkboxes
    measurements = {}
    
    include_steps = st.checkbox("Include Number of Steps", value=True)
    if include_steps:
        measurements['nb_steps'] = st.number_input(
            "Number of Steps",
            min_value=0.0,
            max_value=50000.0,
            value=6500.0,
            step=100.0
        )
    
    include_sleep = st.checkbox("Include Sleep Duration", value=True)
    if include_sleep:
        measurements['sleep_duration'] = st.number_input(
            "Sleep Duration (hours)",
            min_value=0.0,
            max_value=24.0,
            value=7.5,
            step=0.1,
            format="%.1f"
        )
    
    include_hr = st.checkbox("Include Average Night Heart Rate", value=True)
    if include_hr:
        measurements['avg_night_hr'] = st.number_input(
            "Average Night Heart Rate (bpm)",
            min_value=30.0,
            max_value=150.0,
            value=62.0,
            step=1.0
        )
    
    include_active = st.checkbox("Include Mean Active Time", value=False)
    if include_active:
        measurements['mean_active_time'] = st.number_input(
            "Mean Active Time (minutes)",
            min_value=0.0,
            max_value=1440.0,
            value=45.0,
            step=1.0
        )
    
    include_moderate = st.checkbox("Include Moderate Active Minutes", value=False)
    if include_moderate:
        measurements['nb_moderate_active_minutes'] = st.number_input(
            "Moderate Active Minutes",
            min_value=0.0,
            max_value=1440.0,
            value=30.0,
            step=1.0
        )

st.markdown("---")

# Generate Report Button
if st.button("📄 Generate PDF Report", type="primary"):
    if not measurements:
        st.error("Please include at least one biomarker measurement.")
    elif normative_df is None:
        st.error("Normative data not loaded. Cannot generate report.")
    else:
        patient_info = {
            'name': patient_name if patient_name else 'Not specified',
            'age': age,
            'gender': gender,
            'region': region,
            'bmi': bmi
        }
        
        # Calculate z-scores for each included biomarker
        z_scores = {}
        errors = []
        
        for biomarker, value in measurements.items():
            try:
                result = normalizer_model.compute_normative_position(
                    value=value,
                    biomarker=biomarker,
                    age_group=age,
                    region=region,
                    gender=gender,
                    bmi=bmi,
                    normative_df=normative_df
                )
                z_scores[biomarker] = result
            except Exception as e:
                errors.append(f"{BIOMARKER_LABELS.get(biomarker, biomarker)}: {str(e)}")
        
        if errors:
            for err in errors:
                st.warning(f"Z-score calculation note: {err}")
        
        if z_scores:
            with st.spinner("Generating PDF report..."):
                pdf_buffer = generate_pdf_report(patient_info, measurements, z_scores)
            
            st.success("✅ PDF report generated successfully!")
            
            # Report Preview
            st.subheader("Report Preview")
            
            with st.expander("View Report Contents", expanded=True):
                st.markdown("### Demographics")
                st.markdown(f"- **Age:** {age} years")
                st.markdown(f"- **Gender:** {gender}")
                st.markdown(f"- **Region:** {region}")
                st.markdown(f"- **BMI:** {bmi}")
                
                st.markdown("### Measurements & Z-Scores")
                
                # Create columns for z-score display
                num_scores = len(z_scores)
                if num_scores > 0:
                    cols = st.columns(min(num_scores, 3))
                    
                    for idx, (biomarker, data) in enumerate(z_scores.items()):
                        with cols[idx % 3]:
                            label = BIOMARKER_LABELS.get(biomarker, biomarker)
                            z = data['z_score']
                            pct = data['percentile']
                            value = measurements[biomarker]
                            
                            # Context-aware interpretation (Average = -0.5 to 0.5)
                            higher_is_better = biomarker in HIGHER_IS_BETTER
                            
                            if higher_is_better:
                                # For steps, sleep, activity: high is good
                                if z < -2:
                                    interp = "Very Low ⚠️"
                                elif z < -0.5:
                                    interp = "Below Average"
                                elif z < 0.5:
                                    interp = "Average"
                                elif z < 2:
                                    interp = "Above Average ✓"
                                else:
                                    interp = "Excellent ✓✓"
                            else:
                                # For HR: low is good
                                if z < -2:
                                    interp = "Very Low ✓✓"
                                elif z < -0.5:
                                    interp = "Below Average ✓"
                                elif z < 0.5:
                                    interp = "Average"
                                elif z < 2:
                                    interp = "Above Average"
                                else:
                                    interp = "Elevated ⚠️"
                            
                            st.metric(
                                label,
                                f"Z = {z:.2f}",
                                f"{pct:.1f}th percentile"
                            )
                            st.caption(f"Value: {value} | {interp}")
                
                # Cohort info
                age_group_str = normalizer_model._categorize_age(age, normative_df)
                bmi_cat = normalizer_model.categorize_bmi(bmi)
                st.markdown("### Reference Population")
                st.markdown(
                    f"Z-scores calculated from normative data: **{region}**, "
                    f"**{gender}**, age group **{age_group_str}**, BMI category **{bmi_cat}**."
                )
            
            # Download button
            filename = f"smartwatch_report_{patient_name.replace(' ', '_') if patient_name else 'patient'}.pdf"
            
            st.download_button(
                label="⬇️ Download PDF Report",
                data=pdf_buffer,
                file_name=filename,
                mime="application/pdf"
            )
        else:
            st.error("Could not calculate z-scores for any biomarkers. Please check your inputs.")

# Information section
st.markdown("---")

st.markdown("### Report Contents")
st.markdown("""
The generated PDF report includes:

1. **Patient Demographics** - Age, gender, region, BMI
2. **Biomarker Measurements** - All selected smartwatch metrics
3. **Z-Score Analysis** - Comparison to normative population data
   - Z-scores and percentiles for each biomarker
   - Visual gauge charts showing position in distribution
   - Interpretation (Very Low → Average → Very High)
4. **Reference Population Info** - Details about the comparison cohort
5. **Classification Guide** - Explanation of z-score interpretation

*All reports include a disclaimer noting educational/research purpose.*
""")

# Z-Score Classification Guide
with st.expander("📊 Z-Score Classification Guide"):
    st.markdown("""
    **How to interpret Z-Scores:**
    
    | Z-Score Range | Classification | Percentile Range |
    |:-------------:|:--------------:|:----------------:|
    | z < -2.0 | Very Low | < 2.3% |
    | -2.0 ≤ z < -0.5 | Below Average | 2.3% - 30.9% |
    | **-0.5 ≤ z < 0.5** | **Average** | **30.9% - 69.1%** |
    | 0.5 ≤ z < 2.0 | Above Average | 69.1% - 97.7% |
    | z ≥ 2.0 | Very High | > 97.7% |
    
    **Context matters:**
    - For **steps, sleep duration, and active minutes**: Higher values are generally better ✓
    - For **heart rate**: Lower resting values are generally better ✓
    
    *A z-score of 0 means you are exactly at the population average for your demographic group.*
    """)

# Footer
st.markdown("---")
st.markdown(
    "*PDF reports are for educational and research purposes. "
    "For detailed questions regarding personal health data, contact your healthcare professionals.*"
)
st.markdown(
    "Built with ❤️ in Düsseldorf. © Lars Masanneck 2026."
)