feat: connect to API

Dockerfile

@@ -11,7 +11,10 @@ ENV PYTHONDONTWRITEBYTECODE 1
 ENV PYTHONUNBUFFERED 1
 
 # Install virtualenv and create a virtual environment
-RUN pip install --no-cache-dir -U poetry && \
+RUN apt-get update && \
+    apt-get install --no-install-recommends -y ffmpeg libsm6 libxext6 && \
+    rm -rf /var/lib/apt/lists/* && \
+    pip install --no-cache-dir -U poetry && \
     poetry config virtualenvs.create false
 
 # Copy the poetry.lock and pyproject.toml files

app/📣 Alagamentos em Tempo Real.py

@@ -1,6 +1,11 @@
-from pathlib import Path
+import base64
+from datetime import datetime
+import io
+
 import folium
 import pandas as pd
+from PIL import Image
+import requests
 import streamlit as st
 from streamlit_folium import st_folium
 
@@ -16,31 +21,76 @@
     identificar alagamentos em imagens."""
 )
 
-tmp_data_path = Path(__file__).parent.parent / "data" / "cameras_h3_1.csv"
-chart_data = pd.read_csv(tmp_data_path)[:100]
-# dropna status_right from chart_data
-chart_data = chart_data.dropna(subset=["status_right"])
-# remove all Unnamed columns
-chart_data = chart_data.loc[:, ~chart_data.columns.str.contains("^Unnamed")]
-chart_data.to_csv(index=False)
 
-# center map on the mean of the coordinates
-m = folium.Map(
-    location=[chart_data["latitude"].mean(), chart_data["longitude"].mean()],
-    zoom_start=11,
+def generate_image(image_b64: str) -> Image:
+    """Generate image from base64 string"""
+    image_bytes = base64.b64decode(image_b64)
+    image = Image.open(io.BytesIO(image_bytes))
+    return image
+
+
+if "error" not in st.session_state:
+    st.session_state["error"] = False
+
+try:
+    raw_api_data = requests.get(
+        "https://api.dados.rio/v2/clima_alagamento/alagamento_detectado_ia/"
+    ).json()
+    # raw_api_data = [
+    #     {
+    #         "datetime": "2023-11-14 19:01:26",
+    #         "id_camera": "001",
+    #         "url_camera": "rtsp://url:port",
+    #         "latitude": -22.881833,
+    #         "longitude": -43.371461,
+    #         "image_base64": base64.b64encode(
+    #             open("./data/imgs/flooded2.jpg", "rb").read()
+    #         ).decode("utf-8"),
+    #         "ai_classification": [
+    #             {
+    #                 "object": "alagamento",
+    #                 "label": True,
+    #                 "confidence": 0.7,
+    #             },
+    #         ],
+    #     },
+    # ]
+    last_update = requests.get(
+        "https://api.dados.rio/v2/clima_alagamento/ultima_atualizacao_alagamento_detectado_ia/"
+    ).text.strip('"')
+    st.session_state["error"] = False
+except Exception as exc:
+    raw_api_data = []
+    last_update = ""
+    st.session_state["error"] = True
+
+st.markdown(
+    f"""
+    **Conexão com API**: {"✅" if not st.session_state["error"] else "❌"}
+
+    **Última atualização**: {datetime.strptime(last_update, "%Y-%m-%d %H:%M:%S").strftime("%d/%m/%Y %H:%M:%S")}
+""",
 )
 
+chart_data = pd.DataFrame(raw_api_data)
+
+# center map on the mean of the coordinates
+if len(chart_data) > 0:
+    m = folium.Map(
+        location=[chart_data["latitude"].mean(), chart_data["longitude"].mean()],
+        zoom_start=11,
+    )
+else:
+    m = folium.Map(location=[-22.917690, -43.413861], zoom_start=11)
+
 for i in range(0, len(chart_data)):
     folium.Marker(
         location=[chart_data.iloc[i]["latitude"], chart_data.iloc[i]["longitude"]],
         # add nome_da_camera and status to tooltip
         tooltip=f"""
-        Status: {chart_data.iloc[i]['status']}<br>
-        Endereço: {chart_data.iloc[i]['nome_da_camera']}""",
+        ID: {chart_data.iloc[i]['id_camera']}""",
         # change icon color according to status
-        icon=folium.Icon(
-            color="green" if chart_data.iloc[i]["status"] == "normal" else "red"
-        ),
+        icon=folium.Icon(color="orange"),
         # icon=folium.CustomIcon(
         #     icon_data["url"],
         #     icon_size=(icon_data["width"], icon_data["height"]),
@@ -63,23 +113,22 @@
         & (chart_data["longitude"] == obj_coord["lng"])
     ]
 
+    image_b64 = selected_data["image_base64"].values[0]
+
     selected_data = (
-        selected_data[["nome_da_camera", "bairro", "zona", "chuva_15min", "rtsp"]]
+        selected_data[["id_camera", "url_camera"]]
         .rename(
             columns={
-                "nome_da_camera": "Endereço",
-                "bairro": "Bairro",
-                "zona": "Zona",
-                "chuva_15min": "🌧️ Chuva 15min",
-                "rtsp": "🎥 Camera",
+                "id_camera": "Identificador",
+                "url_camera": "🎥 Camera",
             }
         )
         .T
     )
 
     selected_data.columns = ["Infos"]
 
-    st.markdown("## 📷 Imagem da Camera")
-    st.image("./data/imgs/flooded1.jpg")
+    st.markdown("### 📷 Imagem da Câmera")
+    st.image(generate_image(image_b64))
 
     selected_data

pyproject.toml

@@ -11,6 +11,8 @@ streamlit = "^1.28.1"
 folium = "0.14.0"
 streamlit-folium = "^0.15.1"
 streamlit-extras = "^0.3.5"
+streamlit-autorefresh = "^1.0.1"
+pillow = "^10.1.0"
 
 
 [build-system]