Update app.py

app.py

@@ -29,26 +29,45 @@ def analyze_crack(image):
 
 def classify_crack(length, width):
     if length > 150 or width > 20:
-        return "Major"
+        return "🔴 Major"
     elif length > 80 or width > 10:
-        return "Moderate"
+        return "🟠 Moderate"
     else:
-        return "Minor"
+        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."
+    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."
+        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')
+    st.set_page_config(page_title='🏗️ Structural Integrity Analyst', layout='wide', initial_sidebar_state='expanded')
     
-    st.title('🏗️ Structural Integrity Analyst')
+    # 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.sidebar.header("Upload Crack Image")
+    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:
@@ -61,11 +80,11 @@ def main():
         col1, col2 = st.columns(2)
         
         with col1:
-            st.subheader("Uploaded Image")
+            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.subheader("Processed Crack Detection")
+            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")
@@ -74,24 +93,23 @@ def main():
         # Data Analysis
         data = pd.DataFrame(crack_data)
         
-        st.subheader("Crack Metrics & Classification")
-        st.dataframe(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.subheader("Crack Analysis & Recommendations")
+        st.markdown("<h2 class='subheader'>📝 Crack Analysis & Recommendations</h2>", unsafe_allow_html=True)
         for _, row in data.iterrows():
-            st.write(f"**Crack at (X: {row['X']}, Y: {row['Y']})** - {generate_description(row['Severity'])}")
+            st.markdown(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:** 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.")
+        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)
-        fig2 = px.histogram(data, x="Width", color="Severity", title="Crack Width Distribution", nbins=10)
+        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)