app.py

import streamlit as st
import cv2
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
from PIL import Image

def analyze_crack(image):
    # Convert image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # Edge detection
    edges = cv2.Canny(gray, 50, 150)
    
    # Finding contours
    contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    
    # Calculate crack metrics
    crack_data = []
    for cnt in contours:
        length = cv2.arcLength(cnt, True)
        x, y, w, h = cv2.boundingRect(cnt)
        width = w
        severity = classify_crack(length, width)
        crack_data.append({"Length": length, "Width": width, "Severity": severity, "X": x, "Y": y})
    
    return edges, crack_data

def classify_crack(length, width):
    if length > 150 or width > 20:
        return "🔴 Major"
    elif length > 80 or width > 10:
        return "🟠 Moderate"
    else:
        return "🟢 Minor"

def generate_description(severity):
    if "Major" in severity:
        return "🚨 This crack is classified as **Major**, indicating significant structural distress. Major cracks can compromise the integrity of the structure and require **immediate intervention**. These are typically caused by foundation settlement, excessive load, or material failure. **Professional assessment is advised.**"
    elif "Moderate" in severity:
        return "⚠️ This crack is classified as **Moderate**. While not immediately critical, it suggests progressive structural movement or material fatigue. **Monitoring and remedial measures**, such as crack sealing or reinforcement, should be considered."
    else:
        return "✅ This crack is **Minor** and likely due to **surface shrinkage or thermal expansion**. While not structurally concerning, periodic monitoring is recommended to ensure it does not propagate further."

def main():
    st.set_page_config(page_title='🏗️ Structural Integrity Analyst', layout='wide', initial_sidebar_state='expanded')
    
    # Custom Styling
    st.markdown(
        """
        <style>
            .title {
                text-align: center;
                font-size: 36px;
                font-weight: bold;
                color: #003366;
            }
            .subheader {
                font-size: 24px;
                font-weight: bold;
                color: #00509E;
            }
        </style>
        """, unsafe_allow_html=True
    )
    
    st.markdown("<h1 class='title'>🏗️ Structural Integrity Analyst</h1>", unsafe_allow_html=True)
    
    st.sidebar.header("📂 Upload Crack Image")
    uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
    
    if uploaded_file is not None:
        image = Image.open(uploaded_file)
        image = np.array(image)
        
        edges, crack_data = analyze_crack(image)
        
        # Organize layout
        col1, col2 = st.columns(2)
        
        with col1:
            st.markdown("<h2 class='subheader'>📸 Uploaded Image</h2>", unsafe_allow_html=True)
            st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
        
        with col2:
            st.markdown("<h2 class='subheader'>🔍 Processed Crack Detection</h2>", unsafe_allow_html=True)
            fig, ax = plt.subplots()
            ax.imshow(edges, cmap='gray')
            ax.axis("off")
            st.pyplot(fig)
        
        # Data Analysis
        data = pd.DataFrame(crack_data)
        
        st.markdown("<h2 class='subheader'>📊 Crack Metrics & Classification</h2>", unsafe_allow_html=True)
        st.dataframe(data.style.applymap(lambda val: 'background-color: #FFDDC1' if 'Major' in str(val) else ('background-color: #FFF3CD' if 'Moderate' in str(val) else 'background-color: #D4EDDA')))
        
        # Description of Cracks
        st.markdown("<h2 class='subheader'>📝 Crack Analysis & Recommendations</h2>", unsafe_allow_html=True)
        for _, row in data.iterrows():
            st.markdown(f"**Crack at (X: {row['X']}, Y: {row['Y']})** - {generate_description(row['Severity'])}")
        
        # Discussion
        st.markdown("<h2 class='subheader'>💡 Discussion on Crack Severity</h2>", unsafe_allow_html=True)
        st.write("- 🔴 **Major:** Significant structural impact, requires **immediate repair and engineering assessment.**")
        st.write("- 🟠 **Moderate:** Moderate concern, monitoring required, may need **localized reinforcement.**")
        st.write("- 🟢 **Minor:** Surface-level cracks, **not structurally critical** but should be observed over time.")
        
        # Visualization
        fig1 = px.histogram(data, x="Length", color="Severity", title="📏 Crack Length Distribution", nbins=10, color_discrete_map={"🔴 Major": "red", "🟠 Moderate": "orange", "🟢 Minor": "green"})
        fig2 = px.histogram(data, x="Width", color="Severity", title="📐 Crack Width Distribution", nbins=10, color_discrete_map={"🔴 Major": "red", "🟠 Moderate": "orange", "🟢 Minor": "green"})
        
        st.plotly_chart(fig1, use_container_width=True)
        st.plotly_chart(fig2, use_container_width=True)
    
if __name__ == "__main__":
    main()