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import streamlit as st
import os
from groq import Groq
from transformers import ViTForImageClassification, ViTImageProcessor
from sentence_transformers import SentenceTransformer
from PIL import Image
import torch
import numpy as np
from typing import List, Dict
import faiss
import json
# Initialize sentence transformer for embeddings
@st.cache_resource
def init_embedding_model():
return SentenceTransformer('all-MiniLM-L6-v2')
# Initialize Groq client
@st.cache_resource
def init_groq_client():
return Groq(api_key=os.environ.get("GROQ_API_KEY"))
class RAGSystem:
def __init__(self):
self.embedding_model = init_embedding_model()
self.knowledge_base = self.load_knowledge_base()
self.vector_store = self.create_vector_store()
def load_knowledge_base(self) -> List[Dict]:
"""Load and preprocess knowledge base into a list of documents"""
# Your existing knowledge base dictionary
kb = {
"spalling": [
{
"severity": "Critical",
"description": "Severe concrete spalling with exposed reinforcement and section loss",
"repair_method": [
"Install temporary support",
"Remove deteriorated concrete",
"Clean and treat reinforcement",
"Apply corrosion inhibitor",
"Apply bonding agent",
"High-strength repair mortar",
"Surface treatment and waterproofing"
],
"estimated_cost": "Very High ($15,000+)",
"timeframe": "3-4 weeks",
"location": "Primary structural elements",
"required_expertise": "Structural Engineer + Specialist Contractor",
"immediate_action": "Evacuate area, install temporary support, prevent access",
"prevention": "Regular inspections, waterproofing, chloride protection",
"testing_required": ["Core testing", "Reinforcement scanning", "Chloride testing"],
"common_causes": [
"Reinforcement corrosion",
"Freeze-thaw cycles",
"Poor concrete cover",
"Chemical attack"
],
"safety_considerations": [
"Risk of structural failure",
"Falling concrete hazard",
"Worker safety during repairs"
]
},
{
"severity": "Moderate",
"description": "Surface spalling without exposed reinforcement",
"repair_method": [
"Remove loose concrete",
"Surface preparation",
"Apply repair mortar",
"Surface treatment"
],
"estimated_cost": "Medium ($5,000-$10,000)",
"timeframe": "1-2 weeks",
"location": "Non-structural elements",
"required_expertise": "Concrete Repair Specialist",
"immediate_action": "Remove loose material, protect from water ingress",
"prevention": "Surface sealers, proper drainage",
"testing_required": ["Surface adhesion testing", "Moisture testing"],
"common_causes": [
"Surface carbonation",
"Impact damage",
"Poor curing"
],
"safety_considerations": [
"Minor falling debris risk",
"Dust control during repairs"
]
}
],
"reinforcement_corrosion": [
{
"severity": "Critical",
"description": "Severe corrosion with >30% section loss",
"repair_method": [
"Structural support installation",
"Concrete removal around reinforcement",
"Reinforcement replacement",
"Corrosion protection application",
"High-strength concrete repair",
"Cathodic protection installation"
],
"estimated_cost": "Critical ($20,000+)",
"timeframe": "4-6 weeks",
"location": "Load-bearing elements",
"required_expertise": "Senior Structural Engineer",
"immediate_action": "Immediate evacuation, emergency shoring",
"prevention": "Waterproofing, cathodic protection",
"testing_required": [
"Half-cell potential survey",
"Concrete resistivity testing",
"Chloride analysis",
"Carbonation testing"
],
"common_causes": [
"Chloride contamination",
"Carbonation",
"Stray electrical currents",
"Poor concrete quality"
],
"safety_considerations": [
"Structural collapse risk",
"Electrical hazards during testing",
"Confined space entry"
]
}
],
"structural_cracks": [
{
"severity": "High",
"description": "Active structural cracks >5mm width",
"repair_method": [
"Structural analysis",
"Crack monitoring",
"Epoxy injection",
"Carbon fiber reinforcement",
"Load path modification"
],
"estimated_cost": "High ($10,000-$20,000)",
"timeframe": "2-4 weeks",
"location": "Primary structural elements",
"required_expertise": "Structural Engineer",
"immediate_action": "Install crack monitors, restrict loading",
"prevention": "Proper design, joint maintenance",
"testing_required": [
"Crack movement monitoring",
"Load testing",
"Concrete strength testing"
],
"common_causes": [
"Overloading",
"Foundation settlement",
"Thermal movements",
"Design deficiencies"
],
"safety_considerations": [
"Structural stability",
"Water infiltration",
"Working at height"
]
}
],
"water_damage": [
{
"severity": "Medium",
"description": "Active water infiltration with deterioration",
"repair_method": [
"Water source identification",
"Drainage improvement",
"Waterproofing membrane installation",
"Joint sealing",
"Surface treatment"
],
"estimated_cost": "Medium ($5,000-$15,000)",
"timeframe": "1-3 weeks",
"location": "Various locations",
"required_expertise": "Waterproofing Specialist",
"immediate_action": "Water diversion, dehumidification",
"prevention": "Regular maintenance, proper drainage",
"testing_required": [
"Moisture mapping",
"Drainage assessment",
"Permeability testing"
],
"common_causes": [
"Failed waterproofing",
"Poor drainage",
"Joint failure",
"Condensation"
],
"safety_considerations": [
"Slip hazards",
"Electrical safety",
"Mold growth"
]
}
],
"surface_deterioration": [
{
"severity": "Low",
"description": "Surface scaling and deterioration",
"repair_method": [
"Surface cleaning",
"Repair material application",
"Surface treatment",
"Protective coating"
],
"estimated_cost": "Low ($2,000-$5,000)",
"timeframe": "3-5 days",
"location": "Exposed surfaces",
"required_expertise": "Concrete Repair Technician",
"immediate_action": "Clean and protect surface",
"prevention": "Regular maintenance, surface protection",
"testing_required": [
"Surface strength testing",
"Coating adhesion tests"
],
"common_causes": [
"Freeze-thaw damage",
"Chemical exposure",
"Poor finishing",
"Abrasion"
],
"safety_considerations": [
"Dust control",
"Chemical handling",
"PPE requirements"
]
}
],
"alkali_silica_reaction": [
{
"severity": "High",
"description": "Concrete expansion and map cracking due to ASR",
"repair_method": [
"Expansion monitoring",
"Moisture control",
"Crack sealing",
"Surface treatment",
"Structural strengthening"
],
"estimated_cost": "High ($15,000-$25,000)",
"timeframe": "3-5 weeks",
"location": "Concrete elements",
"required_expertise": "Materials Engineer + Structural Engineer",
"immediate_action": "Monitor expansion, control moisture",
"prevention": "Proper aggregate selection, pozzolans",
"testing_required": [
"Petrographic analysis",
"Expansion testing",
"Humidity monitoring"
],
"common_causes": [
"Reactive aggregates",
"High alkali cement",
"Moisture presence",
"Temperature cycles"
],
"safety_considerations": [
"Progressive deterioration",
"Structural integrity",
"Long-term monitoring"
]
}
]
}
# Convert nested knowledge base into flat documents
documents = []
for category, items in kb.items():
for item in items:
# Create a text representation of the document
doc_text = f"Category: {category}\n"
for key, value in item.items():
if isinstance(value, list):
doc_text += f"{key}: {', '.join(value)}\n"
else:
doc_text += f"{key}: {value}\n"
documents.append({
"text": doc_text,
"metadata": {"category": category}
})
return documents
def create_vector_store(self):
"""Create FAISS vector store from knowledge base"""
# Generate embeddings for all documents
texts = [doc["text"] for doc in self.knowledge_base]
embeddings = self.embedding_model.encode(texts)
# Initialize FAISS index
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(np.array(embeddings).astype('float32'))
return index
def create_vector_store(self):
"""Create FAISS vector store from knowledge base"""
# Generate embeddings for all documents
texts = [doc["text"] for doc in self.knowledge_base]
embeddings = self.embedding_model.encode(texts)
# Initialize FAISS index
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(np.array(embeddings).astype('float32'))
return index
def get_relevant_context(self, query: str, k: int = 3) -> str:
"""Retrieve relevant context based on query"""
# Generate query embedding
query_embedding = self.embedding_model.encode([query])
# Search for similar documents
D, I = self.vector_store.search(np.array(query_embedding).astype('float32'), k)
# Combine relevant documents into context
context = "\n\n".join([self.knowledge_base[i]["text"] for i in I[0]])
return context
def get_groq_response(query: str, context: str) -> str:
"""Get response from Groq LLM"""
client = init_groq_client()
try:
prompt = f"""Based on the following context about construction defects, please answer the question.
Context:
{context}
Question: {query}
Please provide a detailed and specific answer based on the given context."""
response = client.chat.completions.create(
messages=[
{
"role": "system",
"content": "You are a construction defect analysis expert. Provide detailed, accurate answers based on the given context."
},
{
"role": "user",
"content": prompt
}
],
model="llama-3.3-70b-versatile",
)
return response.choices[0].message.content
except Exception as e:
return f"Error: {str(e)}"
ef main():
st.set_page_config(
page_title="Construction Defect RAG Analyzer",
page_icon="🏗️",
layout="wide"
)
st.title("🏗️ Construction Defect RAG Analyzer")
# Initialize RAG system
if 'rag_system' not in st.session_state:
st.session_state.rag_system = RAGSystem()
# File upload for image analysis
uploaded_file = st.file_uploader(
"Upload a construction image",
type=['jpg', 'jpeg', 'png']
)
# Query input
user_query = st.text_input("Ask a question about construction defects:")
if user_query:
with st.spinner("Processing query..."):
# Get relevant context using RAG
context = st.session_state.rag_system.get_relevant_context(user_query)
# Debug view of retrieved context
if st.checkbox("Show retrieved context"):
st.subheader("Retrieved Context")
st.text(context)
# Get response from Groq
st.subheader("AI Assistant Response")
response = get_groq_response(user_query, context)
st.write(response)
if uploaded_file:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image")
# Combine image analysis with RAG
col1, col2 = st.columns(2)
with col1:
st.subheader("Image Analysis")
# Image analysis placeholder
st.info("Image analysis results would appear here")
with col2:
st.subheader("AI Assistant Response")
if user_query: # Only show response if there's a query
# Get relevant context from knowledge base
context = st.session_state.rag_system.get_relevant_context(user_query)
# Get response from Groq
with st.spinner("Getting AI response..."):
response = get_groq_response(user_query, context)
st.write(response)
# Display knowledge base sections
if st.checkbox("Show Knowledge Base"):
st.subheader("Available Knowledge Base")
kb_data = st.session_state.rag_system.knowledge_base
for doc in kb_data:
category = doc["metadata"]["category"]
with st.expander(category.title()):
st.text(doc["text"])
if __name__ == "__main__":
main() |