Model and revision pickers, test-set evaluation tab, app/ package

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

Dockerfile

@@ -16,7 +16,8 @@ WORKDIR $HOME/app
 COPY --chown=user pyproject.toml uv.lock ./
 RUN uv sync --locked
 
-COPY --chown=user app.py examples.json ./
+COPY --chown=user main.py ./
+COPY --chown=user app ./app
 
 EXPOSE 7860
-CMD ["python", "app.py"]
+CMD ["python", "main.py"]

README.md

@@ -48,6 +48,40 @@ Le dépôt du modèle est privé. Le Space le lit via un secret `HF_TOKEN`
 (Settings → Secrets), qui doit avoir un accès en lecture à ce dépôt. Les secrets
 ne sont pas visibles par les visiteurs du Space.
 
+## Choix du modèle et de la révision
+
+Les dépôts de l'organisation dont le nom commence par `breakdown-risk-` sont
+listés au démarrage, et chaque commit du dépôt choisi est proposé comme
+révision — de quoi comparer deux entraînements successifs sans redéployer. `↻`
+recharge la liste après un nouveau push de `train.py`.
+
+Les deux sorties de `train.py` sont acceptées : la présence de `model_head.pkl`
+sélectionne le chemin SetFit, sinon le modèle est chargé comme un
+`AutoModelForSequenceClassification`.
+
+L'onglet **Évaluation** rejoue le split de test et affiche la matrice de
+confusion puis les cas un par un, erreurs d'abord.
+
+## Structure
+
+```
+main.py              lancement
+app/config.py        constantes, noms affichés
+app/turns.py         fenêtre des derniers tours
+app/hub.py           dépôts, révisions, téléchargements
+app/predictors.py    SetFit ou AutoModel selon les fichiers du dépôt
+app/evaluation.py    split de test, exactitude, tableaux
+app/handlers.py      fonctions appelées par l'interface
+app/ui.py            Blocks Gradio
+app/cmd_enter.js     ⌘/Ctrl + Entrée
+```
+
+## Raccourcis
+
+⇧ Entrée déclenche `submit` nativement sur un `Textbox` multiligne. ⌘/Ctrl +
+Entrée n'existe pas dans Gradio et passe par `app/cmd_enter.js`, chargé via
+`launch(js=...)` — la méthode décrite dans le guide *Custom CSS and JS*.
+
 ## Dépendances
 
 SDK Docker plutôt que Gradio, pour que les versions viennent d'un `uv.lock` :

app.py

@@ -1,107 +0,0 @@
-import json
-import os
-import time
-from pathlib import Path
-
-import gradio as gr
-import joblib
-from huggingface_hub import hf_hub_download
-from sentence_transformers import SentenceTransformer
-
-MODEL_REPO = "bee2link/breakdown-risk-paraphrase-multilingual-MiniLM-L12-v2"
-DATASET_REPO = "bee2link/breakdown-risk"
-CALLER_TURNS = 3
-TOKEN = os.environ.get("HF_TOKEN")
-
-LABEL_NAMES = {"risk": "Risque de panne", "no_risk": "Pas de risque"}
-
-HEADER = f"""
-# Risque d'immobilisation
-
-Classe les **tours de parole de l'appelant** d'un appel entrant en `risk` — le
-véhicule est probablement immobilisé, il faut un dépannage ou un créneau urgent
-— ou `no_risk` — le client peut rouler, un rendez-vous normal suffit.
-
-<sub>SetFit · encodeur `paraphrase-multilingual-MiniLM-L12-v2` + tête logistique
-· entraîné sur [`{DATASET_REPO}`](https://huggingface.co/datasets/{DATASET_REPO})
-· 45/46 sur le split de test · seuls les {CALLER_TURNS} derniers tours sont
-lus</sub>
-"""
-
-PLACEHOLDER = "Oui bonjour.\nMa voiture ne démarre plus depuis ce matin."
-
-
-def from_hub(filename: str) -> Path:
-    return Path(hf_hub_download(MODEL_REPO, filename, token=TOKEN))
-
-
-encoder = SentenceTransformer(MODEL_REPO, token=TOKEN)
-head = joblib.load(from_hub("model_head.pkl"))
-LABELS = json.loads(from_hub("config_setfit.json").read_text())["labels"]
-EXAMPLES = json.loads(Path("examples.json").read_text(encoding="utf-8"))
-
-
-def last_turns(transcript: str) -> list[str]:
-    lines = [line.strip() for line in transcript.splitlines() if line.strip()]
-    return lines[-CALLER_TURNS:]
-
-
-def score(text: str) -> dict[str, float]:
-    probabilities = head.predict_proba(encoder.encode([text]))[0]
-    return {LABEL_NAMES[name]: float(p) for name, p in zip(LABELS, probabilities)}
-
-
-def classify(transcript: str) -> tuple[dict[str, float], str, str]:
-    """Score a caller's turns for breakdown risk."""
-    turns = last_turns(transcript)
-    if not turns:
-        return {}, "", ""
-
-    text = "\n".join(turns)
-    started = time.perf_counter()
-    scores = score(text)
-    return scores, text, f"`{(time.perf_counter() - started) * 1000:.0f} ms`"
-
-
-with gr.Blocks(title="Risque d'immobilisation") as demo:
-    gr.Markdown(HEADER)
-
-    with gr.Row():
-        with gr.Column(scale=3):
-            transcript = gr.Textbox(
-                label="Tours de parole de l'appelant",
-                info=f"Un tour par ligne. Seuls les {CALLER_TURNS} derniers sont classés.",
-                placeholder=PLACEHOLDER,
-                lines=7,
-                max_lines=14,
-            )
-            run = gr.Button("Classer", variant="primary")
-
-        with gr.Column(scale=2):
-            prediction = gr.Label(label="Prédiction", num_top_classes=2)
-            window = gr.Textbox(
-                label="Ce que le modèle lit",
-                lines=3,
-                interactive=False,
-                buttons=["copy"],
-            )
-            latency = gr.Markdown()
-
-    outputs = [prediction, window, latency]
-
-    gr.Examples(
-        examples=[[row["text"]] for row in EXAMPLES],
-        example_labels=[f"{LABEL_NAMES[row['gold']]} — {row['id']}" for row in EXAMPLES],
-        label="Exemples du split de test",
-        inputs=transcript,
-        outputs=outputs,
-        fn=classify,
-        cache_examples=True,
-        cache_mode="lazy",
-    )
-
-    gr.on([run.click, transcript.submit], classify, transcript, outputs, api_name="classify")
-
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)

app/cmd_enter.js

@@ -0,0 +1,9 @@
+() => {
+    document.addEventListener('keydown', (event) => {
+        if (event.key !== 'Enter' || !(event.metaKey || event.ctrlKey)) return;
+        const button = document.querySelector('#run-classify button, button#run-classify');
+        if (!button) return;
+        event.preventDefault();
+        button.click();
+    });
+}

app/config.py

@@ -0,0 +1,13 @@
+import os
+
+ORG = "bee2link"
+MODEL_SEARCH = "breakdown-risk-"
+DATASET_REPO = "bee2link/breakdown-risk"
+CALLER_TURNS = 3
+TOKEN = os.environ.get("HF_TOKEN")
+
+LABEL_NAMES = {"risk": "Risque de panne", "no_risk": "Pas de risque"}
+
+
+def display(label: str) -> str:
+    return LABEL_NAMES.get(label, label)

app/evaluation.py

@@ -0,0 +1,70 @@
+import time
+from functools import lru_cache
+from typing import NamedTuple
+
+import numpy as np
+from datasets import load_dataset
+
+from .config import DATASET_REPO, TOKEN, display
+from .predictors import Predictor, load
+
+
+class Report(NamedTuple):
+    repo: str
+    revision: str
+    hits: int
+    total: int
+    ms_per_case: float
+    confusion: list[list]
+    cases: list[list]
+
+
+@lru_cache(maxsize=1)
+def test_split() -> tuple[list[str], list[str], list[str]]:
+    data = load_dataset(DATASET_REPO, split="test", token=TOKEN)
+    names = data.features["label"].names
+    return list(data["id"]), list(data["text"]), [names[i] for i in data["label"]]
+
+
+def confusion(labels: list[str], gold: list[str], predicted: list[str]) -> list[list]:
+    counts = dict.fromkeys(((want, got) for want in labels for got in labels), 0)
+    for pair in zip(gold, predicted):
+        counts[pair] += 1
+    return [[f"réel {display(want)}", *(counts[want, got] for got in labels)] for want in labels]
+
+
+def cases(ids, texts, gold, predicted, probabilities: np.ndarray) -> list[list]:
+    rows = [
+        [
+            "✓" if want == got else "✗",
+            row_id,
+            display(want),
+            display(got),
+            round(float(confidence), 3),
+            text.replace("\n", " / "),
+        ]
+        for row_id, text, want, got, confidence in zip(
+            ids, texts, gold, predicted, probabilities.max(axis=1)
+        )
+    ]
+    return sorted(rows, key=lambda row: (row[0] == "✓", row[4]))
+
+
+def run(repo: str, revision: str, predict: Predictor | None = None) -> Report:
+    predictor = predict if predict is not None else load(repo, revision)
+    ids, texts, gold = test_split()
+
+    started = time.perf_counter()
+    labels, probabilities = predictor(texts)
+    elapsed = time.perf_counter() - started
+
+    predicted = [labels[int(row.argmax())] for row in probabilities]
+    return Report(
+        repo=repo,
+        revision=revision,
+        hits=sum(want == got for want, got in zip(gold, predicted)),
+        total=len(gold),
+        ms_per_case=elapsed * 1000 / len(texts),
+        confusion=confusion(labels, gold, predicted),
+        cases=cases(ids, texts, gold, predicted, probabilities),
+    )

app/handlers.py

@@ -0,0 +1,52 @@
+import json
+import time
+from pathlib import Path
+
+import gradio as gr
+
+from . import evaluation
+from .config import display
+from .hub import model_repos, revisions
+from .predictors import load
+from .text import NO_MODEL
+from .turns import window
+
+EXAMPLES = json.loads((Path(__file__).parent / "examples.json").read_text(encoding="utf-8"))
+
+
+def classify(repo: str, revision: str, transcript: str) -> tuple[dict[str, float], str, str]:
+    """Score a caller's turns for breakdown risk."""
+    text = window(transcript)
+    if not repo or not text:
+        return {}, "", ""
+
+    started = time.perf_counter()
+    labels, probabilities = load(repo, revision)([text])
+    scores = {display(name): float(p) for name, p in zip(labels, probabilities[0])}
+    return scores, text, f"`{(time.perf_counter() - started) * 1000:.0f} ms`"
+
+
+def summary(report: evaluation.Report) -> str:
+    return (
+        f"### {report.hits}/{report.total} — exactitude {report.hits / report.total:.1%}\n"
+        f"`{report.repo}` · `{report.revision}` · {report.ms_per_case:.0f} ms par cas"
+    )
+
+
+def evaluate(repo: str, revision: str, progress=gr.Progress()):
+    if not repo:
+        return NO_MODEL, [], []
+
+    progress(0, desc="Chargement du modèle")
+    report = evaluation.run(repo, revision)
+    return summary(report), report.confusion, report.cases
+
+
+def pick_revision(repo: str) -> gr.Dropdown:
+    choices = revisions(repo)
+    return gr.Dropdown(choices=choices, value=choices[0][1] if choices else None)
+
+
+def pick_model() -> gr.Dropdown:
+    choices = model_repos()
+    return gr.Dropdown(choices=choices, value=choices[0] if choices else None)

app/hub.py

@@ -0,0 +1,31 @@
+from pathlib import Path
+
+from huggingface_hub import HfApi, hf_hub_download
+
+from .config import MODEL_SEARCH, ORG, TOKEN
+
+api = HfApi(token=TOKEN)
+
+
+def model_repos() -> list[str]:
+    return sorted(model.id for model in api.list_models(author=ORG, search=MODEL_SEARCH))
+
+
+def revisions(repo: str) -> list[tuple[str, str]]:
+    if not repo:
+        return []
+    commits = api.list_repo_commits(repo, token=TOKEN)
+    return [("main (dernier)", "main"), *(_commit_choice(c) for c in commits)]
+
+
+def _commit_choice(commit) -> tuple[str, str]:
+    label = f"{commit.commit_id[:7]} · {commit.title[:44]} · {commit.created_at:%Y-%m-%d}"
+    return label, commit.commit_id
+
+
+def download(repo: str, filename: str, revision: str) -> Path:
+    return Path(hf_hub_download(repo, filename, revision=revision, token=TOKEN))
+
+
+def has_file(repo: str, filename: str, revision: str) -> bool:
+    return filename in api.list_repo_files(repo, revision=revision, token=TOKEN)

app/predictors.py

@@ -0,0 +1,48 @@
+import json
+from collections.abc import Callable
+from functools import lru_cache
+
+import joblib
+import numpy as np
+import torch
+from sentence_transformers import SentenceTransformer
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+from .config import TOKEN
+from .hub import download, has_file
+
+Predictor = Callable[[list[str]], tuple[list[str], np.ndarray]]
+
+
+def setfit(repo: str, revision: str) -> Predictor:
+    encoder = SentenceTransformer(repo, revision=revision, token=TOKEN)
+    head = joblib.load(download(repo, "model_head.pkl", revision))
+    labels = json.loads(download(repo, "config_setfit.json", revision).read_text())["labels"]
+
+    def predict(texts: list[str]):
+        return labels, head.predict_proba(encoder.encode(texts))
+
+    return predict
+
+
+def automodel(repo: str, revision: str) -> Predictor:
+    tokenizer = AutoTokenizer.from_pretrained(repo, revision=revision, token=TOKEN)
+    model = AutoModelForSequenceClassification.from_pretrained(
+        repo, revision=revision, token=TOKEN
+    ).eval()
+    labels = [model.config.id2label[i] for i in range(model.config.num_labels)]
+
+    def predict(texts: list[str]):
+        batch = tokenizer(texts, truncation=True, padding=True, return_tensors="pt")
+        with torch.no_grad():
+            logits = model(**batch).logits
+        return labels, torch.softmax(logits, dim=-1).numpy()
+
+    return predict
+
+
+@lru_cache(maxsize=4)
+def load(repo: str, revision: str) -> Predictor:
+    if has_file(repo, "model_head.pkl", revision):
+        return setfit(repo, revision)
+    return automodel(repo, revision)

app/text.py

@@ -0,0 +1,22 @@
+from .config import CALLER_TURNS, DATASET_REPO, ORG
+
+HEADER = f"""
+# Risque d'immobilisation
+
+Classe les **tours de parole de l'appelant** d'un appel entrant en `risk` — le
+véhicule est probablement immobilisé, il faut un dépannage ou un créneau urgent
+— ou `no_risk` — le client peut rouler, un rendez-vous normal suffit.
+
+<sub>Modèles publiés par `train.py` dans l'organisation `{ORG}` · évalués sur le
+split de test de [`{DATASET_REPO}`](https://huggingface.co/datasets/{DATASET_REPO})
+· seuls les {CALLER_TURNS} derniers tours sont lus</sub>
+"""
+
+PLACEHOLDER = "Oui bonjour.\nMa voiture ne démarre plus depuis ce matin."
+
+TRANSCRIPT_INFO = (
+    f"Un tour par ligne. Seuls les {CALLER_TURNS} derniers sont classés. "
+    "⇧ Entrée ou ⌘/Ctrl + Entrée pour classer."
+)
+
+NO_MODEL = "Aucun modèle sélectionné."

app/turns.py

@@ -0,0 +1,10 @@
+from .config import CALLER_TURNS
+
+
+def last_turns(transcript: str, count: int = CALLER_TURNS) -> list[str]:
+    lines = [line.strip() for line in transcript.splitlines() if line.strip()]
+    return lines[-count:]
+
+
+def window(transcript: str) -> str:
+    return "\n".join(last_turns(transcript))

app/ui.py

@@ -0,0 +1,101 @@
+from pathlib import Path
+
+import gradio as gr
+
+from .config import display
+from .handlers import EXAMPLES, classify, evaluate, pick_model, pick_revision
+from .hub import model_repos, revisions
+from .text import HEADER, PLACEHOLDER, TRANSCRIPT_INFO
+
+CMD_ENTER_JS = (Path(__file__).parent / "cmd_enter.js").read_text(encoding="utf-8")
+
+
+def selectors() -> tuple[gr.Dropdown, gr.Dropdown, gr.Button]:
+    repos = model_repos()
+    repo = repos[0] if repos else None
+    revs = revisions(repo) if repo else []
+    model = gr.Dropdown(label="Modèle", choices=repos, value=repo, scale=3)
+    revision = gr.Dropdown(
+        label="Révision",
+        choices=revs,
+        value=revs[0][1] if revs else None,
+        scale=4,
+    )
+    return model, revision, gr.Button("↻", scale=0, min_width=48)
+
+
+def classify_tab() -> tuple[gr.Textbox, gr.Button, list]:
+    with gr.Row():
+        with gr.Column(scale=3):
+            transcript = gr.Textbox(
+                label="Tours de parole de l'appelant",
+                info=TRANSCRIPT_INFO,
+                placeholder=PLACEHOLDER,
+                lines=7,
+                max_lines=14,
+            )
+            run = gr.Button("Classer", variant="primary", elem_id="run-classify")
+            gr.Examples(
+                examples=[[row["text"]] for row in EXAMPLES],
+                example_labels=[f"{display(row['gold'])} — {row['id']}" for row in EXAMPLES],
+                label="Exemples du split de test",
+                inputs=transcript,
+            )
+
+        with gr.Column(scale=2):
+            prediction = gr.Label(label="Prédiction", num_top_classes=2)
+            reads = gr.Textbox(
+                label="Ce que le modèle lit",
+                lines=3,
+                interactive=False,
+                buttons=["copy"],
+            )
+            latency = gr.Markdown()
+
+    return transcript, run, [prediction, reads, latency]
+
+
+def evaluation_tab() -> tuple[gr.Button, list]:
+    run = gr.Button("Évaluer sur le split de test", variant="primary")
+    score = gr.Markdown()
+    matrix = gr.Dataframe(
+        label="Matrice de confusion",
+        headers=["", "prédit risque", "prédit pas de risque"],
+        interactive=False,
+    )
+    cases = gr.Dataframe(
+        label="Cas (erreurs d'abord)",
+        headers=["", "id", "attendu", "prédit", "confiance", "texte"],
+        datatype=["str", "str", "str", "str", "number", "str"],
+        wrap=True,
+        interactive=False,
+    )
+    return run, [score, matrix, cases]
+
+
+def build() -> gr.Blocks:
+    with gr.Blocks(title="Risque d'immobilisation") as demo:
+        gr.Markdown(HEADER)
+
+        with gr.Row():
+            model, revision, refresh = selectors()
+        selection = [model, revision]
+
+        with gr.Tab("Classer"):
+            transcript, run, results = classify_tab()
+
+        with gr.Tab("Évaluation"):
+            evaluate_button, report = evaluation_tab()
+
+        model.change(pick_revision, model, revision)
+        refresh.click(pick_model, None, model)
+        gr.on(
+            [run.click, transcript.submit],
+            classify,
+            [*selection, transcript],
+            results,
+            api_name="classify",
+        )
+        evaluate_button.click(evaluate, selection, report, api_name="evaluate")
+
+    return demo

main.py

@@ -0,0 +1,4 @@
+from app.ui import CMD_ENTER_JS, build
+
+if __name__ == "__main__":
+    build().launch(server_name="0.0.0.0", server_port=7860, js=CMD_ENTER_JS)

pyproject.toml

@@ -7,6 +7,8 @@ requires-python = "==3.12.*"
 dependencies = [
     "gradio==6.20.0",
     "sentence-transformers>=5.6.1",
+    "transformers>=5",
+    "datasets>=3",
     "scikit-learn>=1.5",
     "joblib>=1.4",
     "huggingface-hub>=0.30",

uv.lock

@@ -8,6 +8,64 @@ resolution-markers = [
     "sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 
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+name = "aiohttp"
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 source = { virtual = "." }
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     { name = "gradio" },
     { name = "huggingface-hub" },
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