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import os
import json
import streamlit as st
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
from transformers import pipeline
from reportlab.lib.pagesizes import A4
from reportlab.platypus import Paragraph, SimpleDocTemplate, Spacer
from reportlab.lib.styles import getSampleStyleSheet
# Load milestones data
with open('milestones.json', 'r') as f:
milestones = json.load(f)
# Age categories for dropdown selection
age_categories = {
"Up to 2 months": 2,
"Up to 4 months": 4,
"Up to 6 months": 6,
"Up to 9 months": 9,
"Up to 1 year": 12,
"Up to 15 months": 15,
"Up to 18 months": 18,
"Up to 2 years": 24,
"Up to 30 months": 30,
"Up to 3 years": 36,
"Up to 4 years": 48,
"Up to 5 years": 60
}
# Initialize FAISS and Sentence Transformer
model = SentenceTransformer('all-MiniLM-L6-v2')
def create_faiss_index(data):
descriptions = []
age_keys = []
for age, categories in data.items():
for entry in categories:
descriptions.append(entry['description'])
age_keys.append(int(age))
embeddings = model.encode(descriptions, convert_to_numpy=True)
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(embeddings)
return index, descriptions, age_keys
index, descriptions, age_keys = create_faiss_index(milestones)
# Function to retrieve the closest milestone
def retrieve_milestone(user_input):
user_embedding = model.encode([user_input], convert_to_numpy=True)
_, indices = index.search(user_embedding, 1)
return descriptions[indices[0][0]] if indices[0][0] < len(descriptions) else "No relevant milestone found."
# Initialize IBM Granite Model
ibm_model = pipeline("text-generation", model="ibm-granite", max_length=512)
def generate_response(user_input, child_age):
relevant_milestone = retrieve_milestone(user_input)
prompt = (f"The child is {child_age} months old. Based on the given traits: {user_input}, "
f"determine whether the child is meeting expected milestones. "
f"Relevant milestone: {relevant_milestone}. "
"If there are any concerns, suggest steps the parents can take. ")
response = ibm_model(prompt)
return response[0]['generated_text']
# Streamlit UI Styling
st.set_page_config(page_title="Tiny Triumphs Tracker", page_icon="👶", layout="wide")
st.markdown("""
<style>
.stApp { background-color: #1e1e2e; color: #ffffff; }
.stTitle { text-align: center; color: #ffcc00; font-size: 36px; font-weight: bold; }
</style>
""", unsafe_allow_html=True)
st.markdown("<h1 class='stTitle'>👶 Tiny Triumphs Tracker</h1>", unsafe_allow_html=True)
st.markdown("Track your child's key growth milestones from birth to 5 years and detect early developmental concerns.", unsafe_allow_html=True)
# User selects child's age
selected_age = st.selectbox("📅 Select child's age:", list(age_categories.keys()))
child_age = age_categories[selected_age]
# User input for traits and skills
placeholder_text = "Describe your child's behavior and skills."
user_input = st.text_area("✍️ Enter child's behavioral traits and skills:", placeholder=placeholder_text)
def generate_pdf_report(ai_response):
pdf_file = "progress_report.pdf"
doc = SimpleDocTemplate(pdf_file, pagesize=A4)
styles = getSampleStyleSheet()
elements = []
elements.append(Paragraph("Child Development Progress Report", styles['Title']))
elements.append(Spacer(1, 12))
elements.append(Paragraph("Development Insights:", styles['Heading2']))
elements.append(Spacer(1, 10))
response_parts = ai_response.split('\n')
for part in response_parts:
part = part.strip().lstrip('0123456789.- ')
if part:
elements.append(Paragraph(f"• {part}", styles['Normal']))
elements.append(Spacer(1, 5))
disclaimer = "This report is AI-generated and is for informational purposes only. "
elements.append(Spacer(1, 12))
elements.append(Paragraph(disclaimer, styles['Italic']))
doc.build(elements)
return pdf_file
if st.button("🔍 Analyze", help="Click to analyze the child's development milestones"):
ai_response = generate_response(user_input, child_age)
st.subheader("📊 Development Insights:")
st.markdown(f"<div style='background-color:#44475a; color:#ffffff; padding: 15px; border-radius: 10px;'>{ai_response}</div>", unsafe_allow_html=True)
pdf_file = generate_pdf_report(ai_response)
with open(pdf_file, "rb") as f:
st.download_button(label="📥 Download Progress Report", data=f, file_name="progress_report.pdf", mime="application/pdf")
st.warning("⚠️ The results provided are generated by AI and should be interpreted with caution. Please consult a pediatrician for professional advice.")
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