app.py

import streamlit as st
import pandas as pd
import json
import datetime
import os
import plotly.express as px
from fpdf import FPDF

st.set_page_config(page_title="BRA Research Academy - Bra Fit Survey", layout="centered")

st.title("👙 BRA-FIT Survey (BRA Research Academy)")

tab1, tab2 = st.tabs(["📝 Survey Form", "📊 Visualizations"])

# Create folder to store surveys
if not os.path.exists("survey_data"):
    os.makedirs("survey_data")

# File to store and track BRAID counter
braid_counter_file = "braid_counter.txt"
if not os.path.exists(braid_counter_file):
    with open(braid_counter_file, "w") as f:
        f.write("1000")  # Initial BRAID number

# Function to get the next BRAID in serial order
def get_next_braid():
    with open(braid_counter_file, "r") as f:
        current_braid = int(f.read().strip())
    next_braid = current_braid + 1
    with open(braid_counter_file, "w") as f:
        f.write(str(next_braid))
    return f"BRAID{current_braid}"

# PDF class
class PDF(FPDF):
    def header(self):
        self.set_font('Arial', 'B', 14)
        self.cell(0, 10, 'BRA-FIT Survey Report', ln=True, align='C')
        self.ln(10)

    def chapter_title(self, title):
        self.set_font('Arial', 'B', 12)
        self.cell(0, 10, title, ln=True)
        self.ln(4)

    def chapter_body(self, body):
        self.set_font('Arial', '', 12)
        self.multi_cell(0, 10, body)
        self.ln()

def generate_pdf(data, braid):
    if isinstance(data, str):
        data = json.loads(data)  # Convert string back to dict if needed
    pdf = PDF()
    pdf.add_page()
    pdf.chapter_title(f"Braid Number: {braid}")
    for section, details in data.items():
        pdf.chapter_title(section)
        body = "\n".join([f"{k}: {v}" for k, v in details.items()])
        pdf.chapter_body(body)
    return pdf.output(dest='S').encode('latin-1')

with tab1:
    st.header("Section A: Demographics & Background")
    age = st.number_input("Age", min_value=20, max_value=60)
    occupation = st.text_input("Occupation")
    city_state = st.text_input("City & State")
    body_type = st.selectbox("Body Type", ["Petite", "Tall", "Plus-size", "Average"])
    activity_level = st.selectbox("Activity Level", ["Sedentary", "Active", "Highly Active"])
    professional_fitting = st.radio("Have you ever had a professional bra fitting?", ["Yes", "No"])

    st.header("Section B: Breast & Body Details")
    breast_size = st.text_input("Approximate Breast Size (if known)")
    breast_shape = st.selectbox(
        "Breast Shape",
        ["Round", "Bell-shaped", "East-West", "Side set", "Teardrop", "Asymmetrical", "Athletic", "Relaxed"]
    )
    asymmetry = st.radio("Any Asymmetry?", ["No", "Yes - Left Larger", "Yes - Right Larger"])
    underbust_cm = st.number_input("Underbust Measurement (cm)")
    bust_cm = st.number_input("Bust Measurement (cm)")
    rib_cage_shape = st.text_input("Rib Cage Shape (broad, narrow, protruding, flat)")

    st.header("Section C: Bra Usage & Habits")
    current_bra_size = st.text_input("Current Bra Size")
    preferred_brands = st.text_input("Preferred Brands")
    preferred_styles = st.multiselect(
        "Preferred Bra Styles",
        ["T-shirt", "Balconette", "Plunge", "Bralette", "Sports", "Full coverage", "Others"]
    )
    fit_issues = st.multiselect(
        "Common Fit Issues",
        ["Tight band", "Spillage", "Straps dig in", "Underwire pokes", "Gaping cups", "Center gore not tacking"]
    )
    replace_frequency = st.selectbox(
        "How often do you replace your bras?",
        ["Every 6 months", "Every year", "Every 2+ years", "Rarely"]
    )
    dispose_method = st.text_input("How do you usually dispose of old bras?")
    open_to_try = st.radio("Open to trying new bra styles/sizes?", ["Yes", "No"])
    willing_for_trial = st.radio("Willing to participate in bra trial & feedback?", ["Yes", "No"])

    # Submit button
    if st.button("Submit and Generate Report"):
        # Check if any required field is empty
        form_data = {
            "Section A: Demographics & Background": {
                "Age": age,
                "Occupation": occupation,
                "City & State": city_state,
                "Body Type": body_type,
                "Activity Level": activity_level,
                "Professional Bra Fitting": professional_fitting
            },
            "Section B: Breast & Body Details": {
                "Approximate Breast Size": breast_size,
                "Breast Shape": breast_shape,
                "Asymmetry": asymmetry,
                "Underbust Measurement (cm)": underbust_cm,
                "Bust Measurement (cm)": bust_cm,
                "Rib Cage Shape": rib_cage_shape
            },
            "Section C: Bra Usage & Habits": {
                "Current Bra Size": current_bra_size,
                "Preferred Brands": preferred_brands,
                "Preferred Styles": preferred_styles,
                "Common Fit Issues": fit_issues,
                "Replace Frequency": replace_frequency,
                "Dispose Method": dispose_method,
                "Open to Trying New Styles/Sizes": open_to_try,
                "Willing to Participate in Trials": willing_for_trial
            }
        }

        # Validate if all fields are filled
        missing_fields = []
        for section, details in form_data.items():
            for field, value in details.items():
                if not value:
                    missing_fields.append(f"{field} in {section}")
        
        if missing_fields:
            st.error(f"Missing required fields in: {', '.join(missing_fields)}. Please fill them out.")
        else:
            st.success("Form submitted successfully!")

            # Get next BRAID in serial order
            unique_id = get_next_braid()
            timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")

            # Convert data for saving
            json_data = json.dumps(form_data, indent=4)
            flat_data = {}
            for section, items in form_data.items():
                for k, v in items.items():
                    if isinstance(v, list):
                        v = ", ".join(v)
                    flat_data[k] = v
            df = pd.DataFrame([flat_data])

            # Save JSON to memory
            json_file = json.dumps(form_data, indent=4)
            
            # Save PDF to memory
            pdf_file = generate_pdf(form_data, unique_id)

            # Provide download buttons for the reports
            st.download_button(
                label="📄 Download Report as JSON",
                data=json_file,
                file_name=f"{unique_id}_bra_fit_survey.json",
                mime="application/json"
            )

            st.download_button(
                label="📄 Download Report as PDF",
                data=pdf_file,
                file_name=f"{unique_id}_bra_fit_survey.pdf",
                mime="application/pdf"
            )

            st.download_button(
                label="📄 Download Report as CSV",
                data=df.to_csv(index=False).encode('utf-8'),
                file_name=f"{unique_id}_bra_fit_survey.csv",
                mime="text/csv"
            )

with tab2:
    st.header("📊 Visualizations from Survey Data")

    # Load all JSON files
    all_data = []
    for file in os.listdir("survey_data"):
        if file.endswith(".json"):
            with open(os.path.join("survey_data", file)) as f:
                data = json.load(f)
                flat_record = {}
                for section, items in data.items():
                    for k, v in items.items():
                        if isinstance(v, list):
                            v = ", ".join(v)
                        flat_record[k] = v
                all_data.append(flat_record)

    if all_data:
        df = pd.DataFrame(all_data)

        st.subheader("Age Distribution")
        st.bar_chart(df['Age'])

        st.subheader("Preferred Styles")
        if 'Preferred Styles' in df.columns:
            preferred_styles_series = df['Preferred Styles'].str.split(", ").explode()
            style_counts = preferred_styles_series.value_counts().reset_index()
            style_counts.columns = ['Style', 'Count']
            fig = px.pie(style_counts, names='Style', values='Count', title='Preferred Bra Styles')
            st.plotly_chart(fig)

        st.subheader("Fit Issues")
        if 'Common Fit Issues' in df.columns:
            fit_issues_series = df['Common Fit Issues'].str.split(", ").explode()
            issues_counts = fit_issues_series.value_counts().reset_index()
            issues_counts.columns = ['Issue', 'Count']
            fig2 = px.bar(issues_counts, x='Issue', y='Count', title='Common Fit Issues')
            st.plotly_chart(fig2)

        st.subheader("Activity Level Distribution")
        if 'Activity Level' in df.columns:
            activity_counts = df['Activity Level'].value_counts().reset_index()
            activity_counts.columns = ['Activity Level', 'Count']
            fig3 = px.pie(activity_counts, names='Activity Level', values='Count', title='Activity Levels')
            st.plotly_chart(fig3)

    else:
        st.info("No survey data found. Please submit the survey form first.")