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_lengths = [cv2.arcLength(cnt, True) for cnt in contours]
    crack_widths = [cv2.boundingRect(cnt)[2] for cnt in contours]
    
    return edges, crack_lengths, crack_widths

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 main():
    st.set_page_config(page_title='Structural Integrity Analyst', layout='wide', initial_sidebar_state='expanded')
    
    st.title('🏗️ Structural Integrity Analyst')
    
    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_lengths, crack_widths = analyze_crack(image)
        
        # Classification
        classifications = [classify_crack(l, w) for l, w in zip(crack_lengths, crack_widths)]
        
        # Organize layout
        col1, col2 = st.columns(2)
        
        with col1:
            st.subheader("Uploaded Image")
            st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
        
        with col2:
            st.subheader("Processed Crack Detection")
            fig, ax = plt.subplots()
            ax.imshow(edges, cmap='gray')
            ax.axis("off")
            st.pyplot(fig)
        
        # Data Analysis
        data = pd.DataFrame({
            "Crack Length (pixels)": crack_lengths,
            "Crack Width (pixels)": crack_widths,
            "Severity": classifications
        })
        
        st.subheader("Crack Metrics & Classification")
        st.dataframe(data)
        
        # Discussion Tab
        st.subheader("Discussion")
        st.write("Cracks are classified based on their length and width:")
        st.write("- **Major:** Cracks exceeding 150 pixels in length or 20 pixels in width indicate severe damage and require immediate attention.")
        st.write("- **Moderate:** Cracks between 80-150 pixels in length or 10-20 pixels in width are moderate and should be monitored closely.")
        st.write("- **Minor:** Cracks below 80 pixels in length or 10 pixels in width are minor and may not require immediate intervention but should be observed over time.")
        
        # Visualization
        fig1 = px.histogram(data, x="Crack Length (pixels)", color="Severity", title="Crack Length Distribution", nbins=10)
        fig2 = px.histogram(data, x="Crack Width (pixels)", color="Severity", title="Crack Width Distribution", nbins=10)
        
        st.plotly_chart(fig1, use_container_width=True)
        st.plotly_chart(fig2, use_container_width=True)
    
if __name__ == "__main__":
    main()