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import streamlit as st
import torch
from transformers import ViTForImageClassification, ViTImageProcessor, pipeline
from PIL import Image
import pandas as pd
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceHub
from langchain_community.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
import os
# Page config
st.set_page_config(
page_title="Building Damage Analysis",
page_icon="🏗️",
layout="wide"
)
# Initialize models
@st.cache_resource
def load_models():
# Vision model
from huggingface_hub import login
login(token="HUGGINGFACE_API_TOKEN")
damage_model = ViTForImageClassification.from_pretrained(
"microsoft/vit-base-patch16-224",
use_auth_token=True
)
processor = ViTImageProcessor.from_pretrained("microsoft/vit-base-patch16-224")
# Text model
llm = HuggingFaceHub(
repo_id="google/flan-t5-large",
model_kwargs={"temperature": 0.7, "max_length": 512}
)
embeddings = HuggingFaceEmbeddings(
model_name='sentence-transformers/all-MiniLM-L6-v2'
)
return damage_model, processor, embeddings, llm
# Sample data - in production, you'd load this from a proper dataset
SAMPLE_DATA = [
{
"repair_description": "Major wall crack requiring structural repair. Steel plate reinforcement needed.",
"repair_cost": 5000,
"damage_type": "Wall Crack"
},
{
"repair_description": "Concrete beam damage with exposed rebar. Requires immediate attention.",
"repair_cost": 7500,
"damage_type": "Beam Damage"
},
{
"repair_description": "Foundation settling causing structural issues. Need underpinning.",
"repair_cost": 15000,
"damage_type": "Foundation Issue"
}
]
def setup_rag(embeddings, llm):
# Create documents from sample data
documents = [
Document(
page_content=f"{item['repair_description']} Cost: ${item['repair_cost']}",
metadata={'cost': item['repair_cost'], 'damage_type': item['damage_type']}
)
for item in SAMPLE_DATA
]
# Create vector store
vectorstore = FAISS.from_documents(documents, embeddings)
# Create prompt template
template = """
Analyze building damage and provide repair recommendations based on this context:
{context}
For damage type: {question}
Provide:
1. Damage assessment
2. Repair steps
3. Safety considerations
4. Estimated cost range
"""
prompt = PromptTemplate(template=template, input_variables=["context", "question"])
# Create QA chain
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectorstore.as_retriever(search_kwargs={'k': 2}),
chain_type_kwargs={"prompt": prompt}
)
return qa_chain
def process_image(image, model, processor):
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
return predictions[0].tolist()
def main():
st.title("🏗️ Building Damage Detection & Analysis")
st.markdown("""
Upload a photo of building damage for AI analysis and repair recommendations.
""")
# Load models on first run
if 'models_loaded' not in st.session_state:
with st.spinner('Loading AI models...'):
damage_model, processor, embeddings, llm = load_models()
qa_chain = setup_rag(embeddings, llm)
st.session_state['models_loaded'] = True
st.session_state['models'] = (damage_model, processor, qa_chain)
damage_model, processor, qa_chain = st.session_state['models']
# File upload
uploaded_file = st.file_uploader("Upload building damage photo", type=["jpg", "jpeg", "png"])
if uploaded_file:
# Display image
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
with st.spinner('Analyzing damage...'):
# Process image
predictions = process_image(image, damage_model, processor)
damage_types = ["Wall Crack", "Beam Damage", "Foundation Issue",
"Roof Damage", "Structural Damage"]
# Show results
st.subheader("Detected Damage Types")
for damage_type, prob in zip(damage_types, predictions):
if prob > 0.2:
st.metric(damage_type, f"{prob:.1%}")
with st.spinner(f'Generating analysis for {damage_type}...'):
analysis = qa_chain.invoke(damage_type)
st.markdown(f"### Analysis for {damage_type}")
st.markdown(analysis['result'])
if __name__ == "__main__":
main() |