Add docs

.github/setup.sh

@@ -1,4 +1,4 @@
-python -m pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
+python -m pip install torch torchvision
 python -m pip install -e ".[demo]"
 python -m pip install --upgrade streamlit
 sudo apt-get update && sudo apt-get install libgl1

.pre-commit-config.yaml

@@ -3,6 +3,7 @@ repos:
     rev: v5.0.0
     hooks:
       - id: check-added-large-files
+        exclude: docs
       - id: check-case-conflict
       - id: check-json
       - id: check-merge-conflict

README.md

@@ -9,31 +9,31 @@
   </picture>
 </p>
 
-# OSM-AI-helper: a Blueprint by Mozilla.ai for contributing to Open Street Map with the help of AI
+# OSM-AI-helper: a Blueprint by Mozilla.ai for contributing to OpenStreetMap with the help of AI
 
 [![](https://dcbadge.limes.pink/api/server/YuMNeuKStr?style=flat)](https://discord.gg/YuMNeuKStr)
 [![Docs](https://github.com/mozilla-ai/osm-ai-helper/actions/workflows/docs.yaml/badge.svg)](https://github.com/mozilla-ai/osm-ai-helper/actions/workflows/docs.yaml/)
 [![Tests](https://github.com/mozilla-ai/osm-ai-helper/actions/workflows/tests.yaml/badge.svg)](https://github.com/mozilla-ai/osm-ai-helper/actions/workflows/tests.yaml/)
 [![Ruff](https://github.com/mozilla-ai/osm-ai-helper/actions/workflows/lint.yaml/badge.svg?label=Ruff)](https://github.com/mozilla-ai/osm-ai-helper/actions/workflows/lint.yaml/)
 
+<img src="./images/osm-ai-helper-diagram.png" width="800" alt="osm-ai-helper Diagram" />
+
 📘 To explore this project further and discover other Blueprints, visit the [**Blueprints Hub**](https://developer-hub.mozilla.ai/).
 
 👉 📖 For more detailed guidance on using this project, please visit our [**Docs here**](https://mozilla-ai.github.io/osm-ai-helper/)
 
 ### Built with
 
-- Python 3.10+
-- [Ultralytics](https://github.com/ultralytics/ultralytics)
-- [SAM 2](https://github.com/facebookresearch/sam2)
+![Python](https://img.shields.io/badge/Python-3.10%2B-blue) [![Ultralytics](https://img.shields.io/badge/Ultralytics-008080?logo=ultralytics&logoColor=white)](https://ultralytics.com/) [![SAM 2](https://img.shields.io/badge/SAM%202-0099FF?logo=meta&logoColor=white)](https://segment-anything.com/)
 
 
 ## Quick-start
 
-Get started right away finding swimming pools and contributing them to Open Street Map:
+Get started right away finding swimming pools and contributing them to OpenStreetMap:
 
-| Find Swimming Pools |
-| ------------------- |
-| [![Run Inference](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mozilla-ai/osm-ai-helper/blob/main/demo/run_inference.ipynb)
+| Google Colab | HuggingFace Spaces  | GitHub Codespaces |
+| -------------| ------------------- | ----------------- |
+| [![Run Inference](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mozilla-ai/osm-ai-helper/blob/main/demo/run_inference.ipynb) | [![Try on Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Try%20on-Spaces-blue)](https://huggingface.co/spaces/mozilla-ai/osm-ai-helper) | [![Try on Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=928839264&skip_quickstart=true&machine=standardLinux32gb) |
 
 You can also create your own dataset and finetune a new model for a different use case:
 

demo/app.py

@@ -4,6 +4,8 @@
 
 import folium
 import streamlit as st
+from branca.element import MacroElement
+from jinja2 import Template
 from loguru import logger
 from huggingface_hub import hf_hub_download
 from PIL import Image
@@ -24,6 +26,34 @@ def write(self, message):
         return
 
 
+@st.fragment
+def show_map():
+    class LatLngPopup(MacroElement):
+        _template = Template(
+            """
+                {% macro script(this, kwargs) %}
+                    var {{this.get_name()}} = L.popup();
+                    function latLngPop(e) {
+                        {{this.get_name()}}
+                            .setLatLng(e.latlng)
+                            .setContent(e.latlng.lat.toFixed(4) + ", " + e.latlng.lng.toFixed(4))
+                            .openOn({{this._parent.get_name()}});
+                        }
+                    {{this._parent.get_name()}}.on('click', latLngPop);
+                {% endmacro %}
+                """
+        )
+
+        def __init__(self):
+            super().__init__()
+            self._name = "LatLngPopup"
+
+    m = folium.Map(location=[42.2489, -8.5117], zoom_start=11, tiles="OpenStreetMap")
+    m.add_child(LatLngPopup())
+
+    st_folium(m, height=400, width=800)
+
+
 @st.fragment
 def inference(lat_lon, margin):
     with st.spinner("Downloading model..."):
@@ -35,7 +65,7 @@ def inference(lat_lon, margin):
         )
     with st.spinner("Running inference..."):
         output_path, existing, new, missed = run_inference(
-            model_file="models/model.pt",
+            yolo_model_file="models/model.pt",
             output_dir="results",
             lat_lon=lat_lon,
             margin=margin,
@@ -97,31 +127,39 @@ def upload_results(output_path):
         )
 
 
-st.title("Open Street Map AI Helper")
+st.title("OpenStreetMap AI Helper")
 
 st.divider()
 
 st.subheader("Click on the map to select a latitude and longitude")
 
-m = folium.Map(location=[42.2489, -8.5117], zoom_start=11, tiles="OpenStreetMap")
+show_map()
 
-st_data = st_folium(m, width=725)
+lat_lon = st.text_input("Paste the copied (latitude, longitude)")
 
-if st_data.get("last_clicked"):
-    lat = st_data["last_clicked"]["lat"]
-    lon = st_data["last_clicked"]["lng"]
-    st.write(f"Last Clicked: {lat}, {lon}")
+if st.button("Run Inference") and lat_lon:
+    logger.add(StreamlitHandler(), format="<level>{message}</level>")
 
-    if st.button("Run Inference"):
-        streamlit_handler = StreamlitHandler()
-        logger.add(streamlit_handler, format="<level>{message}</level>")
+    lat, lon = lat_lon.split(",")
+    output_path, new = inference(
+        lat_lon=(float(lat.strip()), float(lon.strip())), margin=3
+    )
 
-        output_path, new = inference(lat_lon=(lat, lon), margin=3)
+    if new:
+        st.divider()
+        st.header("Review `new` polygons")
+        st.markdown(
+            "Every `new` polygon will be displayed at the center of the image in `yellow`."
+        )
+        st.markdown(
+            "Polygons in other colors are those already existing in OpenStreetMap and they just "
+            "indicate whether the model has found them (`green`) or missed them (`red`)."
+        )
+        for new in Path(output_path).glob("*.json"):
+            handle_polygon(new)
 
-        if new:
-            st.divider()
-            st.header("Review `new` polygons")
-            for new in Path(output_path).glob("*.json"):
-                handle_polygon(new)
+        logger.add(StreamlitHandler(), format="<level>{message}</level>")
 
-            upload_results(output_path)
+        upload_results(output_path)
+    else:
+        st.warning("No `new` polygons were found. Try a different location.")