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import streamlit as st
from PIL import Image
from model_utils import BugClassifier, get_severity_prediction
# Page configuration
st.set_page_config(
page_title="Bug-O-Scope 🔍🐞",
page_icon="🔍",
layout="wide"
)
# Initialize session state for model
@st.cache_resource
def load_model():
try:
print("Loading model...")
model = BugClassifier()
print("Model loaded successfully")
return model
except Exception as e:
print(f"Error loading model: {str(e)}")
return None
# Ensure model is loaded
if 'model' not in st.session_state:
st.session_state.model = load_model()
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
""")
# Check if model loaded successfully
if st.session_state.model is None:
st.error("Error: Model failed to load. Please try refreshing the page.")
return
# Main content
tab1, tab2 = st.tabs(["Single Bug Analysis", "Bug Comparison"])
with tab1:
single_bug_analysis()
with tab2:
compare_bugs()
def single_bug_analysis():
"""Handle single bug analysis"""
uploaded_file = st.file_uploader("Upload a bug photo", type=['png', 'jpg', 'jpeg'], key="single")
if uploaded_file:
try:
# Load and display image
image = Image.open(uploaded_file)
col1, col2 = st.columns(2)
with col1:
st.image(image, caption="Uploaded Image", use_container_width=True)
with col2:
with st.spinner("Analyzing your bug..."):
# Get predictions
prediction, confidence = st.session_state.model.predict(image)
print(f"Prediction: {prediction}, Confidence: {confidence}")
st.success("Analysis Complete!")
st.markdown("### Identified Species")
st.markdown(f"**{prediction}**")
st.markdown(f"Confidence: {confidence:.2f}%")
# Only show ecological impact for known insects
if prediction != "Unknown Insect" and prediction != "Error Processing Image":
severity = get_severity_prediction(prediction)
st.markdown("### Ecological Impact")
severity_color = {
"Low": "green",
"Medium": "orange",
"High": "red",
"Unknown": "gray"
}
st.markdown(
f"Severity: <span style='color: {severity_color[severity]}'>{severity}</span>",
unsafe_allow_html=True
)
# Display species information
if prediction != "Unknown Insect" and prediction != "Error Processing Image":
st.markdown("### About This Species")
species_info = st.session_state.model.get_species_info(prediction)
st.markdown(species_info)
# Display visualization
st.markdown("### Feature Highlights")
gradcam = st.session_state.model.get_gradcam(image)
st.image(gradcam, caption="Important Features", use_container_width=True)
except Exception as e:
st.error(f"Error processing image: {str(e)}")
st.info("Please try uploading a different image.")
def compare_bugs():
"""Handle bug comparison"""
col1, col2 = st.columns(2)
with col1:
file1 = st.file_uploader("Upload first bug photo", type=['png', 'jpg', 'jpeg'], key="compare1")
if file1:
try:
image1 = Image.open(file1)
st.image(image1, caption="First Bug", use_container_width=True)
except Exception as e:
st.error(f"Error loading first image: {str(e)}")
return
with col2:
file2 = st.file_uploader("Upload second bug photo", type=['png', 'jpg', 'jpeg'], key="compare2")
if file2:
try:
image2 = Image.open(file2)
st.image(image2, caption="Second Bug", use_container_width=True)
except Exception as e:
st.error(f"Error loading second image: {str(e)}")
return
if file1 and file2:
try:
with st.spinner("Generating comparison..."):
# Get predictions
pred1, conf1 = st.session_state.model.predict(image1)
pred2, conf2 = st.session_state.model.predict(image2)
if pred1 not in ["Unknown Insect", "Error Processing Image"] and \
pred2 not in ["Unknown Insect", "Error Processing Image"]:
# 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}%")
gradcam1 = st.session_state.model.get_gradcam(image1)
st.image(gradcam1, caption="Feature Highlights - Bug 1", use_container_width=True)
with comp_col2:
st.markdown(f"**Species 2**: {pred2}")
st.markdown(f"Confidence: {conf2:.2f}%")
gradcam2 = st.session_state.model.get_gradcam(image2)
st.image(gradcam2, caption="Feature Highlights - Bug 2", use_container_width=True)
# Display comparison
st.markdown("### Key Differences")
st.markdown(st.session_state.model.get_species_info(pred1))
st.markdown(st.session_state.model.get_species_info(pred2))
else:
st.warning("Unable to generate meaningful comparison due to low confidence predictions.")
except Exception as e:
st.error(f"Error comparing images: {str(e)}")
st.info("Please try uploading different images or try again.")
if __name__ == "__main__":
main() |