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#app.py
import streamlit as st
from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch
from sentence_transformers import SentenceTransformer
import faiss
import pandas as pd
import os
from pathlib import Path
import json
DAMAGE_TYPES = {
0: {'name': 'spalling', 'risk': 'High'},
1: {'name': 'reinforcement_corrosion', 'risk': 'Critical'},
2: {'name': 'structural_crack', 'risk': 'High'},
3: {'name': 'dampness', 'risk': 'Medium'},
4: {'name': 'no_damage', 'risk': 'Low'}
}
@st.cache_resource
def load_models():
vision_model = ViTForImageClassification.from_pretrained(
"google/vit-base-patch16-224",
num_labels=len(DAMAGE_TYPES),
ignore_mismatched_sizes=True
)
processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
return vision_model, processor, embedding_model
class DamageKnowledgeBase:
def __init__(self, embedding_model):
self.embedding_model = embedding_model
self.load_knowledge_base()
def load_knowledge_base(self):
# Load dataset metadata and embeddings
knowledge_path = Path("data/knowledge_base.json")
if knowledge_path.exists():
with open(knowledge_path, 'r') as f:
self.kb_data = json.load(f)
# Initialize FAISS index
embeddings = torch.load("data/embeddings.pt")
self.index = faiss.IndexFlatL2(embeddings.shape[1])
self.index.add(embeddings.numpy())
else:
self.initialize_knowledge_base()
def initialize_knowledge_base(self):
# Sample knowledge base structure
self.kb_data = {
'spalling': [
{
'description': 'Severe concrete spalling on column surface',
'severity': 'High',
'repair_method': 'Remove damaged concrete, clean reinforcement, apply repair mortar',
'estimated_cost': 'High',
'timeframe': '2-3 weeks',
'similar_cases': ['case_123', 'case_456']
}
],
# Add more damage types...
}
# Create embeddings
texts = []
for damage_type, cases in self.kb_data.items():
for case in cases:
texts.append(f"{damage_type} {case['description']} {case['repair_method']}")
embeddings = self.embedding_model.encode(texts)
self.index = faiss.IndexFlatL2(embeddings.shape[1])
self.index.add(embeddings)
# Save for future use
os.makedirs("data", exist_ok=True)
with open("data/knowledge_base.json", 'w') as f:
json.dump(self.kb_data, f)
torch.save(torch.tensor(embeddings), "data/embeddings.pt")
def query(self, damage_type, confidence):
query = f"damage type: {damage_type}"
query_embedding = self.embedding_model.encode([query])
D, I = self.index.search(query_embedding, k=3)
similar_cases = []
for idx in I[0]:
for damage, cases in self.kb_data.items():
for case in cases:
case_text = f"{damage} {case['description']} {case['repair_method']}"
if len(similar_cases) < 3:
similar_cases.append(case)
return similar_cases
def analyze_damage(image, model, processor):
image = image.convert('RGB')
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0]
return probs
def main():
st.title("Advanced Structural Damage Assessment Tool")
vision_model, processor, embedding_model = load_models()
kb = DamageKnowledgeBase(embedding_model)
uploaded_file = st.file_uploader("Upload structural image", type=['jpg', 'jpeg', 'png'])
if uploaded_file:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Structure", use_column_width=True)
with st.spinner("Analyzing..."):
predictions = analyze_damage(image, vision_model, processor)
col1, col2 = st.columns(2)
with col1:
st.subheader("Damage Assessment")
detected_damages = []
for idx, prob in enumerate(predictions):
confidence = float(prob) * 100
if confidence > 15:
damage_type = DAMAGE_TYPES[idx]['name']
detected_damages.append((damage_type, confidence))
st.write(f"**{damage_type.replace('_', ' ').title()}**")
st.progress(confidence / 100)
st.write(f"Confidence: {confidence:.1f}%")
st.write(f"Risk Level: {DAMAGE_TYPES[idx]['risk']}")
with col2:
st.subheader("Similar Cases & Recommendations")
for damage_type, confidence in detected_damages:
similar_cases = kb.query(damage_type, confidence)
st.write(f"**{damage_type.replace('_', ' ').title()}:**")
for case in similar_cases:
with st.expander(f"Similar Case - {case['severity']} Severity"):
st.write(f"Description: {case['description']}")
st.write(f"Repair Method: {case['repair_method']}")
st.write(f"Estimated Cost: {case['estimated_cost']}")
st.write(f"Timeframe: {case['timeframe']}")
if __name__ == "__main__":
main() |