Create app.py

app.py

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+import streamlit as st
+import torch
+from PIL import Image
+import io
+import numpy as np
+from model_utils import BugClassifier, generate_gradcam, get_severity_prediction
+from transformers import AutoFeatureExtractor
+
+# Page configuration
+st.set_page_config(
+    page_title="Bug-O-Scope 🔍🐞",
+    page_icon="🔍",
+    layout="wide"
+)
+
+# Initialize session state
+if 'model' not in st.session_state:
+    st.session_state.model = BugClassifier()
+    st.session_state.feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
+
+def main():
+    # Header
+    st.title("Bug-O-Scope 🔍🐞")
+    st.markdown("""
+    Welcome to Bug-O-Scope! Upload a picture of an insect to learn more about it.
+    This educational tool helps you identify bugs and understand their role in our ecosystem.
+    """)
+
+    # Sidebar
+    st.sidebar.header("About Bug-O-Scope")
+    st.sidebar.markdown("""
+    Bug-O-Scope is an AI-powered tool that helps you:
+    * 🔍 Identify insects from photos
+    * 📚 Learn about different species
+    * 🌍 Understand their ecological impact
+    * 🔬 Compare different insects
+    """)
+
+    # Main content
+    tab1, tab2 = st.tabs(["Single Bug Analysis", "Bug Comparison"])
+
+    with tab1:
+        single_bug_analysis()
+
+    with tab2:
+        compare_bugs()
+
+def single_bug_analysis():
+    uploaded_file = st.file_uploader("Upload a bug photo", type=['png', 'jpg', 'jpeg'], key="single")
+    
+    if uploaded_file:
+        image = Image.open(uploaded_file)
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.image(image, caption="Uploaded Image", use_column_width=True)
+        
+        with col2:
+            with st.spinner("Analyzing your bug..."):
+                # Get predictions
+                prediction, confidence = st.session_state.model.predict(image)
+                severity = get_severity_prediction(prediction)
+                
+                st.success("Analysis Complete!")
+                st.markdown(f"### Identified Species")
+                st.markdown(f"**{prediction}**")
+                st.markdown(f"Confidence: {confidence:.2f}%")
+                
+                st.markdown("### Ecological Impact")
+                severity_color = {
+                    "Low": "green",
+                    "Medium": "orange",
+                    "High": "red"
+                }
+                st.markdown(f"Severity: <span style='color: {severity_color[severity]}'>{severity}</span>", 
+                           unsafe_allow_html=True)
+
+        # Generate and display species information
+        st.markdown("### About This Species")
+        species_info = st.session_state.model.get_species_info(prediction)
+        st.markdown(species_info)
+
+def compare_bugs():
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        file1 = st.file_uploader("Upload first bug photo", type=['png', 'jpg', 'jpeg'], key="compare1")
+        if file1:
+            image1 = Image.open(file1)
+            st.image(image1, caption="First Bug", use_column_width=True)
+            
+    with col2:
+        file2 = st.file_uploader("Upload second bug photo", type=['png', 'jpg', 'jpeg'], key="compare2")
+        if file2:
+            image2 = Image.open(file2)
+            st.image(image2, caption="Second Bug", use_column_width=True)
+    
+    if file1 and file2:
+        with st.spinner("Generating comparison..."):
+            # Get predictions for both images
+            pred1, conf1 = st.session_state.model.predict(image1)
+            pred2, conf2 = st.session_state.model.predict(image2)
+            
+            # Generate Grad-CAM visualizations
+            gradcam1 = generate_gradcam(image1, st.session_state.model)
+            gradcam2 = generate_gradcam(image2, st.session_state.model)
+            
+            # Display results
+            st.markdown("### Comparison Results")
+            comp_col1, comp_col2 = st.columns(2)
+            
+            with comp_col1:
+                st.markdown(f"**Species 1**: {pred1}")
+                st.markdown(f"Confidence: {conf1:.2f}%")
+                st.image(gradcam1, caption="Feature Highlights - Bug 1", use_column_width=True)
+                
+            with comp_col2:
+                st.markdown(f"**Species 2**: {pred2}")
+                st.markdown(f"Confidence: {conf2:.2f}%")
+                st.image(gradcam2, caption="Feature Highlights - Bug 2", use_column_width=True)
+            
+            # Display comparison information
+            st.markdown("### Key Differences")
+            differences = st.session_state.model.compare_species(pred1, pred2)
+            st.markdown(differences)
+
+if __name__ == "__main__":
+    main()