Create app.py

app.py

@@ -0,0 +1,54 @@
+import streamlit as st
+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+import tempfile
+import os
+
+# ---------------- CONFIG ----------------
+st.set_page_config(page_title="Pothole Detection", layout="wide")
+st.title("🕳️ Pothole Detection using YOLO")
+st.write("Upload an image — the model will detect potholes and mark them.")
+
+# -------- Load YOLO Model --------------
+@st.cache_resource
+def load_model():
+    try:
+        model = YOLO("best.pt")  # your model file
+        return model
+    except Exception as e:
+        st.error(f"Failed to load model: {e}")
+        return None
+
+model = load_model()
+
+if model is None:
+    st.stop()
+
+# -------- File Upload ------------------
+uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_container_width=True)
+
+    with st.spinner("Detecting potholes... ⏳"):
+        # Save temp file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
+            image.save(tmp.name)
+            results = model(tmp.name)
+
+        # Render result image
+        result_img = results[0].plot()  # numpy array (BGR)
+
+        # Convert BGR to RGB
+        result_img_rgb = Image.fromarray(result_img[..., ::-1])
+
+        st.image(result_img_rgb, caption="Detected Potholes ✅", use_container_width=True)
+
+        # Download button
+        result_path = "output_pothole.jpg"
+        result_img_rgb.save(result_path)
+
+        with open(result_path, "rb") as f:
+            st.download_button("📥 Download Result", f, file_name="pothole_detected.jpg")