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| import streamlit as st | |
| from PIL import Image | |
| from modules.thermal_fault_detection import detect_faults | |
| st.set_page_config(page_title="Solar Panel Thermal Fault Detector", layout="centered") | |
| st.title("🔍 Solar Panel Thermal Fault Detection Dashboard") | |
| st.markdown(""" | |
| This AI-powered app analyzes thermal images from drones to detect: | |
| - 🔥 Overheating | |
| - 🌫️ Dust Accumulation | |
| - 🔧 Physical Breakage | |
| """) | |
| uploaded_image = st.file_uploader("Upload a thermal image of a solar panel", type=["jpg", "png", "jpeg", "webp"]) | |
| if uploaded_image: | |
| image = Image.open(uploaded_image).convert("RGB") | |
| st.image(image, caption="Uploaded Thermal Image", use_column_width=True) | |
| with st.spinner("Analyzing for thermal faults..."): | |
| results, annotated_image = detect_faults(image) | |
| if results: | |
| st.error("❗ Faults Detected:") | |
| for fault in results: | |
| st.write(f"- 🔴 **Fault Type:** {fault[0]} | **Confidence:** {fault[1]:.2f}") | |
| st.image(annotated_image, caption="Detected Faults", use_column_width=True) | |
| else: | |
| st.success("✅ No faults detected.") | |