Update app.py

app.py

@@ -17,10 +17,15 @@ def analyze_crack(image):
     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]
+    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_lengths, crack_widths
+    return edges, crack_data
 
 def classify_crack(length, width):
     if length > 150 or width > 20:
@@ -30,6 +35,14 @@ def classify_crack(length, width):
     else:
         return "Minor"
 
+def generate_description(severity):
+    if severity == "Major":
+        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 severity == "Moderate":
+        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')
     
@@ -42,10 +55,7 @@ def main():
         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)]
+        edges, crack_data = analyze_crack(image)
         
         # Organize layout
         col1, col2 = st.columns(2)
@@ -62,28 +72,29 @@ def main():
             st.pyplot(fig)
         
         # Data Analysis
-        data = pd.DataFrame({
-            "Crack Length (pixels)": crack_lengths,
-            "Crack Width (pixels)": crack_widths,
-            "Severity": classifications
-        })
+        data = pd.DataFrame(crack_data)
         
         st.subheader("Crack Metrics & Classification")
         st.dataframe(data)
         
-        # Discussion Tab
-        st.subheader("Discussion")
+        # Description of Cracks
+        st.subheader("Crack Analysis & Recommendations")
+        for _, row in data.iterrows():
+            st.write(f"**Crack at (X: {row['X']}, Y: {row['Y']})** - {generate_description(row['Severity'])}")
+        
+        # Discussion
+        st.subheader("Discussion on Crack Severity")
         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.")
+        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="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)
+        fig1 = px.histogram(data, x="Length", color="Severity", title="Crack Length Distribution", nbins=10)
+        fig2 = px.histogram(data, x="Width", 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()
+    main()