refactor: Improve error management

bases/ecoindex/cli/app.py

@@ -233,9 +233,9 @@ def analyze(
             logger=logger,
         )
 
-        for result, success in analysis_results:
+        for result, error in analysis_results:
             results.append(result)
-            if not success:
+            if error:
                 count_errors += 1
 
             progress.update(task, advance=1)

components/ecoindex/exceptions/scraper.py

@@ -7,5 +7,6 @@ def __init__(self, url: str, status: int, message: str):
         self.message = message
         self.url = url
         self.status = status
+        self.msg = f"{status}: {message}"
 
     pass

components/ecoindex/scraper/helper.py

@@ -12,7 +12,7 @@ def run_page_analysis(
     wait_after_scroll: int = 3,
     wait_before_scroll: int = 3,
     logger=None,
-) -> tuple[Result, bool]:
+) -> tuple[Result, str | None]:
     """Run the page analysis and return the result and a boolean indicating if the analysis was successful"""
     scraper = EcoindexScraper(
         url=str(url),
@@ -22,7 +22,7 @@ def run_page_analysis(
         page_load_timeout=20,
     )
     try:
-        return (run(scraper.get_page_analysis()), True)
+        return (run(scraper.get_page_analysis()), None)
     except Exception as e:
         logger.error(f"{url} -- {e.msg if hasattr(e, 'msg') else e}")
 
@@ -36,7 +36,7 @@ def run_page_analysis(
                 nodes=0,
                 requests=0,
             ),
-            False,
+            e.msg if hasattr(e, "msg") else str(e),
         )
 
 
@@ -47,7 +47,7 @@ def bulk_analysis(
     wait_after_scroll: int = 0,
     wait_before_scroll: int = 0,
     logger=None,
-) -> Generator[tuple[Result, bool], None, None]:
+) -> Generator[tuple[Result, str | None], None, None]:
     with ThreadPoolExecutor(max_workers=max_workers) as executor:
         future_to_analysis = {}
 

components/ecoindex/scraper/scrap.py

@@ -9,6 +9,7 @@
 from ecoindex.models.compute import PageMetrics, Result, ScreenShot, WindowSize
 from ecoindex.models.scraper import MimetypeAggregation, RequestItem, Requests
 from ecoindex.utils.screenshots import convert_screenshot_to_webp, set_screenshot_rights
+from playwright._impl._network import Response
 from playwright.async_api import async_playwright
 from typing_extensions import deprecated
 
@@ -72,7 +73,7 @@ async def scrap_page(self) -> PageMetrics:
                 ignore_https_errors=True,
             )
             response = await self.page.goto(self.url)
-            await self.check_page_response(response)
+            await self.check_page_response(response)  # type: ignore
 
             await self.page.wait_for_load_state()
             sleep(self.wait_before_scroll)
@@ -142,7 +143,7 @@ def get_request_size(self, entry) -> int:
         else:
             return len(json.dumps(entry["response"]).encode("utf-8"))
 
-    async def check_page_response(self, response) -> None:
+    async def check_page_response(self, response: Response) -> None:
         if response and response.status != 200:
             raise EcoindexScraperStatusException(
                 url=self.url,

development/ui.py

@@ -7,19 +7,21 @@
 from loguru import logger
 
 st.set_page_config(
-    page_title="Analyse Ecoindex",
-    page_icon="🌍",
+    page_title="Ecoindex",
+    page_icon="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.866em%22 font-size=%22100%22>🟢</text></svg>",
     initial_sidebar_state="auto",
 )
-st.title("Analyse Ecoindex")
+st.title("Ecoindex")
 
-form_container = st.container()
-results_container = st.container()
+tab_local, tab_website = st.tabs(["Ecoindex Local", "Ecoindex.fr"])
+
+form_container = tab_local.container()
+results_container = tab_local.container()
+form_container.header("Lancer une analyse")
 
 try:
     import playwright  # noqa
 
-    st.toast("Playwright est installé", icon="🎉")
 except ImportError:
     st.toast("Playwright n'est pas installé", icon="⚠️")
     st.info("Pour configurer Playwright, veuillez suivre les instructions suivantes:")
@@ -51,13 +53,31 @@ def run_analysis(
     progress_bar = results_container.progress(
         0, text=f"{nb_analysis} analyse en cours..."
     )
-    for i, (result, success) in enumerate(analysis_results):
+
+    for i, (result, error) in enumerate(analysis_results):
         progress_bar.progress((i + 1) / nb_analysis)
-        analysis.append({"success": success, **result.__dict__})
+        analysis.append({**result.__dict__, "error": error})
 
     progress_bar.empty()
 
     df = pandas.DataFrame(analysis)
+    # Apply colors on error column style
+    # df.style.applymap(lambda x: "color: red" if x else "", subset=["error"])
+
+    # Apply colors on grade column
+    colors = {
+        "A": "#349A47",
+        "B": "#51B84B",
+        "C": "#CADB2A",
+        "D": "#F6EB15",
+        "E": "#FECD06",
+        "F": "#F99839",
+        "G": "#ED2124",
+    }
+    # df = df.style.applymap(
+    #     lambda x: f"color: {colors[x]}" if x else "", subset=["grade"]
+    # )
+
     results_container.header("Résultats de l'analyse")
     results_container.dataframe(
         df,
@@ -134,6 +154,10 @@ def run_analysis(
                 help="Type de page",
                 disabled=True,
             ),
+            "error": st.column_config.TextColumn(
+                "Erreur",
+                help="Erreur lors de l'analyse",
+            ),
         },
         hide_index=True,
     )
@@ -153,31 +177,36 @@ def run_analysis(
 
     with st.expander("Options"):
         col1, col2 = st.columns(2)
-        with col1:
-            wait_before_scroll = st.number_input(
-                label="Attendre avant de scroller",
-                value=3,
-                min_value=0,
-                help="Temps d'attente avant de scroller en secondes pour que la page se charge complètement",
-            )
+        wait_before_scroll = col1.number_input(
+            label="Attendre avant de scroller",
+            value=3,
+            min_value=0,
+            help="Temps d'attente avant de scroller en secondes pour que la page se charge complètement",
+        )
 
-        with col2:
-            wait_after_scroll = st.number_input(
-                label="Attendre après avoir scrollé",
-                value=3,
-                min_value=0,
-                help="Temps d'attente après avoir scrollé en secondes pour que la page se charge complètement",
-            )
+        wait_after_scroll = col2.number_input(
+            label="Attendre après avoir scrollé",
+            value=3,
+            min_value=0,
+            help="Temps d'attente après avoir scrollé en secondes pour que la page se charge complètement",
+        )
         basic_auth = st.text_input(label="Authentification de base", disabled=True)
         session_cookie = st.text_input(label="Cookie de session", disabled=True)
 
     submitted = st.form_submit_button(
         label="Lancer l'analyse",
-        on_click=run_analysis,
-        kwargs={
-            "urls": urls,
-            "sizes": sizes,
-            "wait_before_scroll": wait_before_scroll,
-            "wait_after_scroll": wait_after_scroll,
-        },
     )
+
+    if submitted:
+        run_analysis(
+            urls=urls,
+            sizes=sizes,
+            wait_before_scroll=wait_before_scroll,
+            wait_after_scroll=wait_after_scroll,
+        )
+
+
+container_website_search = tab_website.container()
+container_website_results = tab_website.container()
+
+container_website_search.header("Rechercher un résultat d'analyse")