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| import streamlit as st | |
| import requests | |
| from PIL import Image | |
| # ============================== | |
| # CONFIG | |
| # ============================== | |
| API_BASE = "https://AdarshDS-mold-detection-api.hf.space" | |
| CONVNEXT_API = f"{API_BASE}/predict/v2" | |
| st.set_page_config( | |
| page_title="Mold Detection System", | |
| layout="centered" | |
| ) | |
| # ============================== | |
| # UI HEADER | |
| # ============================== | |
| st.title("🦠 AI Mold Detection System") | |
| st.caption("Powered by ConvNeXt (Advanced Model)") | |
| st.write( | |
| "Upload an image of a wall or ceiling. " | |
| "The image is analyzed using an advanced ConvNeXt-based AI model " | |
| "with uncertainty estimation and self-supervised verification." | |
| ) | |
| file = st.file_uploader( | |
| "Upload Image", | |
| type=["jpg", "png", "jpeg"] | |
| ) | |
| # ============================== | |
| # MAIN LOGIC | |
| # ============================== | |
| if file: | |
| image = Image.open(file).convert("RGB") | |
| st.image(image, caption="Uploaded Image", use_container_width=True) | |
| if st.button("Analyze"): | |
| file_bytes = file.getvalue() | |
| files_payload = { | |
| "file": (file.name, file_bytes, file.type) | |
| } | |
| with st.spinner("🔍 Analyzing image..."): | |
| resp = requests.post(CONVNEXT_API, files=files_payload) | |
| st.markdown("---") | |
| st.subheader("📊 Prediction Result") | |
| if resp.status_code != 200: | |
| st.error("❌ API error while analyzing the image.") | |
| st.text(resp.text) | |
| else: | |
| res = resp.json() | |
| # ============================== | |
| # Main Results | |
| # ============================== | |
| st.metric("Decision", res["decision"]) | |
| st.metric( | |
| "Mold Probability", | |
| res["model_outputs"]["mold_probability"] | |
| ) | |
| st.metric( | |
| "Biological Probability", | |
| res["model_outputs"]["biological_probability"] | |
| ) | |
| # ============================== | |
| # Confidence Checks | |
| # ============================== | |
| st.subheader("📈 Confidence Checks") | |
| cc = res["confidence_checks"] | |
| c1, c2, c3 = st.columns(3) | |
| c1.metric("Uncertainty", cc["uncertainty"]) | |
| c2.metric("Patch Ratio", cc["patch_ratio"]) | |
| c3.metric("DINO Similarity", cc["dino_similarity"]) | |
| # ============================== | |
| # User Feedback | |
| # ============================== | |
| if res["decision"] == "Mold": | |
| st.error( | |
| "❌ Mold detected. " | |
| "Professional remediation is strongly recommended." | |
| ) | |
| elif res["decision"] == "Possible Mold": | |
| st.warning( | |
| "⚠️ Possible mold detected. " | |
| "Human inspection is advised." | |
| ) | |
| else: | |
| st.success("✅ No mold detected.") | |