Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,165 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+# Fichier: app.py
+
+# ===================================================================================
+# WAHIS SCRAPER - VERSION TABLEAU DE BORD STREAMLIT (APPROCHE FINALE ET ROBUSTE)
+# ===================================================================================
+
 import streamlit as st
+import pandas as pd
+import json
+from datetime import datetime
+from pathlib import Path
+import warnings
+import asyncio
+import subprocess
+from playwright.async_api import async_playwright
+from playwright_stealth import stealth_async
+from streamlit_folium import st_folium
+import folium
+
+# --- Configuration de la Page Streamlit ---
+st.set_page_config(layout="wide", page_title="WAHIS Animal Disease Dashboard")
+
+# --- Classes et Fonctions de Scraping (inchangées) ---
+# ... (la logique de scraping est parfaite, on la garde telle quelle)
+class WAHISScraper:
+    def __init__(self): self.logs = []
+    def log(self, message):
+        timestamp = datetime.now().strftime("%H:%M:%S")
+        self.logs.append(f"[{timestamp}] {message}")
+        print(message)
+    async def run_extraction_async(self):
+        self.log("🚀 Lancement de l'extraction en trois phases...")
+        async with async_playwright() as p:
+            browser = None
+            try:
+                self.log("🔧 Lancement d'un navigateur Chromium...")
+                browser = await p.chromium.launch(headless=True, args=["--no-sandbox"])
+                page = await browser.new_page()
+                self.log("🕵️ Application du camouflage 'stealth'...")
+                await stealth_async(page)
+                self.log("🌍 Visite de la page principale pour passer le challenge Cloudflare...")
+                await page.goto("https://wahis.woah.org/#/event-management", wait_until="networkidle", timeout=90000)
+                self.log("🍪 Challenge Cloudflare réussi.")
+                headers = { 'Content-Type': 'application/json', 'Accept': 'application/json', 'clientid': 'OIEwebsite', 'env': 'PRD', 'security-token': 'token', 'type': 'REQUEST' }
+                self.log("--- PHASE 1 : Récupération des rapports ---")
+                list_api_url = "https://wahis.woah.org/api/v1/pi/event/filtered-list?language=fr"
+                payload_list = { "pageNumber": 1, "pageSize": 100, "sortColName": "REP_LAST_UPDATE", "sortColOrder": "DESC", "reportFilters": {}, "languageChanged": False }
+                list_response_json = await page.evaluate("async (args) => (await fetch(args.url, { method: 'POST', headers: args.headers, body: JSON.stringify(args.payload) })).json()", {'url': list_api_url, 'headers': headers, 'payload': payload_list})
+                report_list = list_response_json.get('list', [])
+                if not report_list: raise Exception("Échec de la phase 1.")
+                self.log(f"✅ Phase 1 réussie : {len(report_list)} rapports de base récupérés.")
+                unique_event_ids = sorted(list(set(item['eventId'] for item in report_list if 'eventId' in item)))
+                self.log(f"--- PHASE 2 : Récupération des données GPS pour {len(unique_event_ids)} événements...")
+                outbreaks_api_url = "https://wahis.woah.org/api/v1/pi/map-data/outbreaks-from-event-ids?language=fr"
+                all_outbreaks_data = await page.evaluate("async (args) => (await fetch(args.url, { method: 'POST', headers: args.headers, body: JSON.stringify(args.payload) })).json()", {'url': outbreaks_api_url, 'headers': headers, 'payload': unique_event_ids})
+                self.log(f"✅ Phase 2 réussie : {len(all_outbreaks_data)} foyers récupérés.")
+                unique_outbreak_ids = sorted(list(set(item['outbreakId'] for item in all_outbreaks_data if 'outbreakId' in item)))
+                self.log(f"--- PHASE 3 : Récupération des détails épidémiologiques pour {len(unique_outbreak_ids)} foyers...")
+                additional_info_data = []
+                if unique_outbreak_ids:
+                    additional_info_api_url = "https://wahis.woah.org/api/v1/pi/outbreak/additional-information"
+                    additional_info_data = await page.evaluate("async (args) => (await fetch(args.url, { method: 'POST', headers: args.headers, body: JSON.stringify(args.payload) })).json()", {'url': additional_info_api_url, 'headers': headers, 'payload': unique_outbreak_ids})
+                    self.log(f"✅ Phase 3 réussie : {len(additional_info_data)} fiches de détails récupérées.")
+                return report_list, all_outbreaks_data, additional_info_data, "\n".join(self.logs)
+            finally:
+                if browser and browser.is_connected(): await browser.close()
+                
+# --- Fonctions de Traitement des Données ---
+def process_data(reports, outbreaks, additional_infos):
+    valid_additional_infos = [info for info in additional_infos if isinstance(info, dict)]
+    additional_info_map = {info.get('outbreakId'): info for info in valid_additional_infos}
+    
+    report_map = {report['eventId']: {'disease': report['disease']} for report in reports}
+
+    for outbreak in outbreaks:
+        event_info = report_map.get(outbreak.get('eventId'), {})
+        outbreak['diseaseName'] = event_info.get('disease')
+        outbreak_id = outbreak.get('outbreakId')
+        if outbreak_id in additional_info_map:
+            outbreak.update(additional_info_map[outbreak_id])
+    
+    return pd.DataFrame(outbreaks)
+
+# --- Construction de l'Interface Streamlit ---
+
+st.title("🤖 Tableau de Bord WAHIS")
+st.info("Ce tableau de bord extrait et affiche les derniers foyers de maladies animales signalés à l'Organisation Mondiale de la Santé Animale (WOAH).")
+
+# Utilisation du "session_state" pour stocker les données après le premier chargement
+if 'df_outbreaks' not in st.session_state:
+    st.session_state.df_outbreaks = pd.DataFrame()
+    st.session_state.logs = ""
+    st.session_state.last_click = None
+
+if st.button("🚀 Lancer l'extraction des données"):
+    with st.spinner("Extraction en cours... (cela peut prendre 2-3 minutes)"):
+        scraper = WAHISScraper()
+        reports, outbreaks, additional, logs = asyncio.run(scraper.run_extraction_async())
+        if reports:
+            st.session_state.df_outbreaks = process_data(reports, outbreaks, additional)
+            st.session_state.logs = logs
+            st.success("Extraction terminée avec succès !")
+        else:
+            st.error("L'extraction a échoué. Veuillez consulter les logs.")
+            st.session_state.logs = logs
+
+if not st.session_state.df_outbreaks.empty:
+    df = st.session_state.df_outbreaks
+
+    # --- Barre Latérale avec les Filtres ---
+    st.sidebar.header("🔍 Filtres")
+    all_diseases = ["Toutes"] + sorted(df['diseaseName'].dropna().unique())
+    all_species = ["Toutes"] + sorted(df['species'].dropna().unique())
+    
+    selected_disease = st.sidebar.selectbox("Filtrer par Maladie", all_diseases)
+    selected_species = st.sidebar.selectbox("Filtrer par Espèce", all_species)
+    
+    # Filtrage du DataFrame
+    filtered_df = df.copy()
+    if selected_disease != "Toutes":
+        filtered_df = filtered_df[filtered_df['diseaseName'] == selected_disease]
+    if selected_species != "Toutes":
+        filtered_df = filtered_df[filtered_df['species'] == selected_species]
+
+    # --- Affichage Principal : Carte et Détails ---
+    st.header(f"🗺️ Carte de {len(filtered_df)} Foyers")
+    
+    if filtered_df.empty or not all(k in filtered_df for k in ['latitude', 'longitude']):
+        st.warning("Aucun foyer ne correspond à vos filtres, ou les données GPS sont manquantes.")
+    else:
+        # Création de la carte avec Folium
+        m = folium.Map(location=[filtered_df['latitude'].mean(), filtered_df['longitude'].mean()], zoom_start=2)
+        
+        for _, row in filtered_df.iterrows():
+            popup_html = f"""
+            <b>Lieu:</b> {row.get('locationName', 'N/A')}<br>
+            <b>Maladie:</b> {row.get('diseaseName', 'N/A')}<br>
+            <b>Espèce:</b> {row.get('species', 'N/A')}<br>
+            <b>Cas:</b> {row.get('cases', 0)} | <b>Morts:</b> {row.get('deaths', 0)}
+            """
+            iframe = folium.IFrame(popup_html, width=250, height=100)
+            popup = folium.Popup(iframe, max_width=250)
+            folium.Marker(
+                location=[row['latitude'], row['longitude']],
+                popup=popup,
+                tooltip=row.get('diseaseName', 'N/A')
+            ).add_to(m)
+
+        # Affichage de la carte et récupération du dernier point cliqué
+        map_data = st_folium(m, width='100%')
+        if map_data and map_data['last_object_clicked_popup']:
+            st.session_state.last_click = map_data['last_object_clicked_popup']['html']
+
+    # Affichage des détails du dernier point cliqué
+    if st.session_state.last_click:
+        st.header("📋 Détails du Foyer Sélectionné")
+        st.markdown(st.session_state.last_click, unsafe_allow_html=True)
+        
+    # --- Affichage du tableau de données ---
+    with st.expander("Voir le tableau de données des foyers filtrés"):
+        st.dataframe(filtered_df)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# --- Affichage des logs ---
+with st.expander("Voir le journal d'exécution"):
+    st.text_area("Logs", st.session_state.logs, height=300)