Create app.py

app.py

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+import os
+import json
+import streamlit as st
+import faiss
+import numpy as np
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from sentence_transformers import SentenceTransformer
+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))  # Convert age to int
+    
+    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."
+
+# Load IBM Granite model and tokenizer
+model_name = "ibm/granite-13b-chat"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+granite_model = AutoModelForCausalLM.from_pretrained(
+    model_name, torch_dtype=torch.float16, device_map="auto"
+)
+
+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."
+    )
+
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
+    output = granite_model.generate(**inputs, max_length=512)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# 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; }
+        .stButton > button { background-color: #ffcc00; color: #000; border-radius: 5px; font-weight: bold; }
+        .stSelectbox, .stTextArea { background-color: #2e2e42; color: #ffffff; border-radius: 5px; }
+    </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 = "For example, your child might say simple words like 'mama' and 'dada' and smile when spoken to. They may grasp small objects with their fingers and show excitement during playtime."
+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 = [
+        Paragraph("Child Development Progress Report", styles['Title']),
+        Spacer(1, 12),
+        Paragraph("Development Insights:", styles['Heading2']),
+        Spacer(1, 10)
+    ]
+
+    for part in ai_response.split('\n'):
+        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. "
+                  "It should not be considered a substitute for professional medical advice. "
+                  "Always consult a qualified pediatrician for expert guidance on your child's development.")
+    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.")