Update app.py

app.py

@@ -1,90 +1,59 @@
 import streamlit as st
-import torch
-from transformers import ViTForImageClassification, ViTImageProcessor
+from transformers import pipeline
 from PIL import Image
+import torch
 import numpy as np
-from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.document_loaders import TextLoader
-from langchain.text_splitter import CharacterTextSplitter
-import io
-import json
 
-# Load pre-trained model and processor
-model_name = "google/vit-base-patch16-224-in21k"
-processor = ViTImageProcessor.from_pretrained(model_name)
-model = ViTForImageClassification.from_pretrained(model_name)
+# Initialize classifier
+classifier = pipeline("image-classification", model="microsoft/resnet-50")
 
-# Custom class labels
-damage_types = [
-    "spalling",
-    "reinforcement_corrosion",
-    "flexural_crack",
-    "structural_crack",
-    "dampness",
-    "impact_failure"
-]
+# Page config
+st.set_page_config(page_title="Structural Damage Assessment", layout="wide")
 
-# Initialize FAISS vector store
-embeddings = HuggingFaceEmbeddings()
-knowledge_base = FAISS.load_local("knowledge_base", embeddings)
+def analyze_damage(image):
+    predictions = classifier(image)
+    return predictions
 
-def process_image(image):
-    # Preprocess image
-    inputs = processor(images=image, return_tensors="pt")
+def main():
+    st.title("Structural Damage Assessment Tool")
     
-    # Get model predictions
-    outputs = model(**inputs)
-    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    # Sidebar
+    st.sidebar.header("Upload Options")
+    uploaded_file = st.sidebar.file_uploader("Choose an image", type=['png', 'jpg', 'jpeg'])
     
-    # Get top predictions
-    top_probs, top_indices = torch.topk(probs, len(damage_types))
-    
-    return {
-        damage_types[idx]: float(prob)
-        for idx, prob in zip(top_indices[0], top_probs[0])
-    }
-
-def get_recommendations(damage_type):
-    # Query vector store for recommendations
-    docs = knowledge_base.similarity_search(
-        f"Remedial measures for {damage_type} in building structures",
-        k=3
-    )
-    return [doc.page_content for doc in docs]
-
-# Streamlit UI
-st.title("Structural Damage Assessment Tool")
-
-# File upload
-uploaded_file = st.file_uploader("Upload structural image", type=["jpg", "jpeg", "png"])
-
-if uploaded_file:
-    # Display image
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image", use_column_width=True)
-    
-    # Process image
-    with st.spinner("Analyzing image..."):
-        predictions = process_image(image)
-    
-    # Display results
-    st.subheader("Damage Assessment")
-    for damage_type, probability in predictions.items():
-        st.progress(probability)
-        st.write(f"{damage_type.replace('_', ' ').title()}: {probability:.2%}")
+    if uploaded_file:
+        # Display image
+        image = Image.open(uploaded_file)
+        col1, col2 = st.columns(2)
         
-        # Show recommendations
-        if probability > 0.5:
-            st.subheader(f"Recommendations for {damage_type.replace('_', ' ').title()}")
-            recommendations = get_recommendations(damage_type)
-            for i, rec in enumerate(recommendations, 1):
-                st.write(f"{i}. {rec}")
-    
-    # Generate report
-    st.download_button(
-        "Download Report",
-        json.dumps(predictions, indent=2),
-        "assessment_report.json",
-        "application/json"
-    )
+        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)
+                
+                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}")
+
+if __name__ == "__main__":
+    main()