bio-performx/app/services/page_generator.py

import datetime
import pandas as pd
import matplotlib.pyplot as plt
class PageGenerator:
    def __init__(self, pnoe_df: pd.DataFrame, seca_df: pd.DataFrame, spirometry_df: pd.DataFrame, patient_info: dict):
        self.pnoe_df = pnoe_df
        self.seca_df = seca_df
        self.spirometry_df = spirometry_df
        self.patient_info = patient_info
        
    
    def page_1_context(self):
        # Extract patient information
        patient_name = self.patient_info.get("patient_name", "N/A")
        context = {
            "first_name": patient_name.split()[0] if patient_name else "N/A",
            "surname": patient_name.split()[-1] if patient_name else "N/A",
            "date": datetime.datetime.now().strftime("%B %d, %Y"),
        }

        return context
    
    def page_3_context(self):
        # Example: Extract detailed PNOE data for page 3
        pnoe_details = self.pnoe_df.head(10).to_dict(orient="records")

        context = {
            "pnoe_details": pnoe_details,
        }

        return context
    
    def page_4_context(self):
        # Example: Extract detailed Spirometry data for page 4
        # Filter df_2 for Keirstyn Moran
        patient_data = self.patient_info[self.patient_info['LastName'].str.contains('Moran', case=False, na=False)]
        fat_percentage = patient_data['Adult_FMP'].iloc[0]
        weight_kg = patient_data['Weight'].iloc[0]
        lean_percentage = 100 - fat_percentage

        fat_mass_lbs = weight_kg * (fat_percentage / 100) * 2.20462
        lean_mass_lbs = weight_kg * (lean_percentage / 100) * 2.20462

        total_weight = fat_mass_lbs + lean_mass_lbs
        fat_percentage = (fat_mass_lbs / total_weight) * 100
        lean_percentage = (lean_mass_lbs / total_weight) * 100

        sizes = [fat_percentage, lean_percentage]
        colors = ['#fde3ac', '#ff9966'] # Light yellow/tan and orange from the image

        plt.figure(figsize=(8, 8))

        wedges, texts, autotexts = plt.pie(sizes, autopct='', startangle=90, wedgeprops=dict(width=0.5, edgecolor='w'), colors=colors, labels=['', ''])  # Remove default labels

        plt.text(-1, 1, f'Fat Mass ({fat_mass_lbs:.1f}lbs)\n{fat_percentage:.1f}%', 
                fontsize=14, fontweight='bold', ha='center', va='center',
                bbox=dict(boxstyle="round,pad=0.3", facecolor='white', alpha=0.8))

        plt.text(1, -1, f'Lean Mass ({lean_mass_lbs:.1f}lbs)\n{lean_percentage:.1f}%', 
                fontsize=14, fontweight='bold', ha='center', va='center',
                bbox=dict(boxstyle="round,pad=0.3", facecolor='white', alpha=0.8))

        plt.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle
        spirometry_details = self.spirometry_df.head(10).to_dict(orient="records")

        context = {
            "spirometry_details": spirometry_details,
        }

        return context
    
    def page_5_context(self):
        # Example: Summary and conclusions for page 5
        summary = {
            "pnoe_summary": self.pnoe_df.describe().to_dict(),
            "seca_summary": self.seca_df.describe().to_dict(),
            "spirometry_summary": self.spirometry_df.describe().to_dict(),
        }

        context = {
            "summary": summary,
        }

        return context
    
    def page_6_context(self):
        # Example: Recommendations based on data for page 6
        recommendations = [
            "Increase cardiovascular training.",
            "Incorporate strength training twice a week.",
            "Monitor respiratory function regularly.",
        ]

        context = {
            "recommendations": recommendations,
        }

        return context
    
    def page_7_context(self):
        # Example: Additional notes or references for page 7
        notes = "This report is generated based on the provided data. Consult a healthcare professional for personalized advice."

        context = {
            "notes": notes,
        }

        return context
    
    def page_8_context(self):
        # Example: Contact information and disclaimers for page 8
        contact_info = {
            "clinic_name": "Health Clinic",
            "address": "123 Wellness St, Healthy City, HC 12345",
            "phone": "(123) 456-7890",
            "email": "contact@healthclinic.com"
        }

        context = {
            "contact_info": contact_info,
        }

        return context

    def page_9_context(self):
        # Example: Graphs and visualizations for page 9
        graphs = [
            "graph1.png",
            "graph2.png",
            "graph3.png"
        ]

        context = {
            "graphs": graphs,
        }

        return context
    
    def page_10_context(self):
        # Example: Final remarks and next steps for page 10
        final_remarks = "Thank you for using our health assessment services. We look forward to assisting you in your health journey."

        context = {
            "final_remarks": final_remarks,
        }

        return context
    
    def page_11_context(self):
        # Example: Additional resources and references for page 11
        resources = [
            {"title": "Healthy Living Guide", "link": "http://example.com/healthy-living"},
            {"title": "Exercise Tips", "link": "http://example.com/exercise-tips"},
            {"title": "Nutrition Advice", "link": "http://example.com/nutrition-advice"}
        ]

        context = {
            "resources": resources,
        }

        return context
    
    def page_12_context(self):
        # Example: Feedback and survey information for page 12
        feedback_info = "We value your feedback. Please visit http://example.com/feedback to share your experience."

        context = {
            "feedback_info": feedback_info,
        }

        return context
    
    def page_13_context(self):
        # Example: Acknowledgments and credits for page 13
        acknowledgments = "This report was generated using data analysis techniques and the expertise of our medical team."

        context = {
            "acknowledgments": acknowledgments,
        }

        return context
    
    def page_18_context(self):
        # Example: Legal disclaimers and terms for page 18
        legal_disclaimer = "This report is for informational purposes only and does not constitute medical advice."

        context = {
            "legal_disclaimer": legal_disclaimer,
        }

        return context