Update app.py

app.py

@@ -1,59 +1,46 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import pipeline, AutoModelForImageClassification, AutoImageProcessor
 from PIL import Image
 import torch
-import numpy as np
 
-# Initialize classifier
-classifier = pipeline("image-classification", model="microsoft/resnet-50")
+# Initialize models
+model_name = "microsoft/resnet-50"
+image_processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+classifier = pipeline("image-classification", model=model, image_processor=image_processor)
 
-# Page config
-st.set_page_config(page_title="Structural Damage Assessment", layout="wide")
+damage_types = {
+    'spalling': ['Remove loose material', 'Apply repair mortar', 'Seal surface'],
+    'cracking': ['Measure crack width', 'Install crack monitors', 'Apply sealant'],
+    'corrosion': ['Clean exposed steel', 'Apply rust converter', 'Paint protective coating'],
+    'dampness': ['Locate water source', 'Fix drainage issues', 'Apply waterproofing']
+}
 
-def analyze_damage(image):
-    predictions = classifier(image)
-    return predictions
+def analyze_image(image):
+    return classifier(image)
 
 def main():
-    st.title("Structural Damage Assessment Tool")
+    st.title("Structural Damage Assessment")
     
-    # Sidebar
-    st.sidebar.header("Upload Options")
-    uploaded_file = st.sidebar.file_uploader("Choose an image", type=['png', 'jpg', 'jpeg'])
+    uploaded_file = st.file_uploader("Upload structural image", type=['jpg', 'jpeg', 'png'])
     
     if uploaded_file:
-        # Display image
         image = Image.open(uploaded_file)
-        col1, col2 = st.columns(2)
+        st.image(image, caption="Uploaded Image")
         
-        with col1:
-            st.image(image, caption="Uploaded Structure", use_column_width=True)
-        
-        # Analyze image
-        with col2:
-            st.header("Analysis Results")
-            with st.spinner("Analyzing..."):
-                results = analyze_damage(image)
+        with st.spinner("Analyzing..."):
+            results = analyze_image(image)
+            
+            for result in results:
+                confidence = result['score'] * 100
+                st.progress(confidence / 100)
+                st.write(f"{result['label']}: {confidence:.1f}%")
                 
-                for result in results:
-                    score = result['score'] * 100
-                    st.progress(int(score))
-                    st.write(f"{result['label']}: {score:.2f}%")
-        
-        # Recommendations
-        st.header("Recommendations")
-        damages = {
-            'Spalling': ['Repair exposed areas', 'Apply protective coating'],
-            'Cracks': ['Monitor crack width', 'Apply crack sealant'],
-            'Corrosion': ['Remove rust', 'Apply anti-corrosive treatment'],
-            'Dampness': ['Improve drainage', 'Apply waterproofing']
-        }
-        
-        for damage, remedies in damages.items():
-            if any(damage.lower() in r['label'].lower() for r in results):
-                st.subheader(f"{damage} Remedial Measures:")
-                for remedy in remedies:
-                    st.write(f"- {remedy}")
+                for damage_type, remedies in damage_types.items():
+                    if damage_type in result['label'].lower():
+                        st.subheader("Recommended Actions:")
+                        for remedy in remedies:
+                            st.write(f"• {remedy}")
 
 if __name__ == "__main__":
     main()