Upload folder using huggingface_hub

README.md

@@ -1,13 +1,19 @@
 ---
-title: Doctobert Fr Base
-emoji: 👁
-colorFrom: gray
-colorTo: blue
+title: DoctoBERT-fr-base
+emoji: 🏥
+colorFrom: yellow
+colorTo: purple
 sdk: gradio
 sdk_version: 6.19.0
-python_version: '3.12'
 app_file: app.py
-pinned: false
+short_description: Fill-mask demo for French medical RoBERTa
+python_version: "3.12"
+startup_duration_timeout: 30m
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# DoctoBERT-fr-base
+
+French medical RoBERTa (111M parameters) fill-mask demo. Enter a French medical sentence
+containing a `<mask>` token and the model predicts the most likely completions.
+
+Model: [doctolib-lab/doctobert-fr-base](https://huggingface.co/doctolib-lab/doctobert-fr-base)

app.py

@@ -0,0 +1,122 @@
+import spaces  # MUST come before torch / any CUDA-touching import
+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForMaskedLM, top_k_top_p_filtering
+import torch.nn.functional as F
+
+MODEL_ID = "doctolib-lab/doctobert-fr-base"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForMaskedLM.from_pretrained(MODEL_ID).to("cuda")
+model.eval()
+
+MASK_TOKEN = tokenizer.mask_token  # "<mask>"
+
+
+@spaces.GPU(duration=15)
+def predict_mask(text: str, top_k: int = 5):
+    """Predict the most likely tokens to fill the <mask> in a French medical sentence.
+
+    Args:
+        text: A French medical sentence containing one <mask> token.
+        top_k: Number of top predictions to return.
+    """
+    if MASK_TOKEN not in text:
+        return {"error": f"Le texte doit contenir le jeton {MASK_TOKEN}."}
+
+    inputs = tokenizer(text, return_tensors="pt")
+    input_ids = inputs["input_ids"]
+
+    mask_index = (input_ids[0] == tokenizer.mask_token_id).nonzero(as_tuple=True)[0]
+    if len(mask_index) == 0:
+        return {"error": "Aucun jeton <mask> trouvé dans le texte."}
+    mask_pos = mask_index[0].item()
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+
+    mask_logits = logits[0, mask_pos, :]
+    probs = F.softmax(mask_logits, dim=-1)
+    topk_probs, topk_indices = torch.topk(probs, top_k)
+
+    results = []
+    for i in range(top_k):
+        token_id = topk_indices[i].item()
+        token = tokenizer.convert_ids_to_tokens(token_id)
+        # Clean up the SentencePiece prefix
+        clean_token = token.replace("▁", "").strip()
+        prob = topk_probs[i].item()
+        results.append({"token": clean_token, "score": round(prob, 4)})
+
+    # Also build a filled-in sentence with the top prediction
+    top_token_id = topk_indices[0].unsqueeze(0)
+    filled_ids = input_ids[0].clone()
+    filled_ids[mask_pos] = top_token_id
+    filled_text = tokenizer.decode(filled_ids, skip_special_tokens=True)
+
+    return {
+        "predictions": results,
+        "best_prediction": filled_text,
+    }
+
+
+CSS = """
+#col-container { max-width: 900px; margin: 0 auto; }
+.dark .gradio-container { color: var(--body-text-color); }
+"""
+
+with gr.Blocks(theme=gr.themes.Citrus(), css=CSS) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(
+            """
+            # 🏥 DoctoBERT-fr-base
+            **DoctoBERT-fr-base** est un encodeur médical français (RoBERTa, 111M de paramètres)
+            pré-entraîné sur des données biomédicales et cliniques françaises.
+            Démo de **fill-mask** : donnez une phrase médicale contenant `<mask>`
+            et le modèle prédit les jetons les plus probables.
+
+            🔗 [Modèle sur le Hub](https://huggingface.co/doctolib-lab/doctobert-fr-base)
+            """
+        )
+
+        with gr.Row():
+            text_input = gr.Textbox(
+                label="Phrase médicale (avec <mask>)",
+                placeholder=f"Le patient souffre d'une {MASK_TOKEN} aiguë.",
+                scale=4,
+            )
+            run_btn = gr.Button("Prédire", variant="primary", scale=1)
+
+        with gr.Accordion("Paramètres avancés", open=False):
+            top_k_input = gr.Slider(
+                minimum=1, maximum=20, value=5, step=1,
+                label="Nombre de prédictions (top-k)",
+            )
+
+        best_pred_output = gr.Textbox(label="Meilleure prédiction", interactive=False)
+        predictions_output = gr.JSON(label="Top-k prédictions")
+
+        run_btn.click(
+            fn=predict_mask,
+            inputs=[text_input, top_k_input],
+            outputs=[predictions_output, best_pred_output],
+            api_name="predict",
+        )
+
+        gr.Examples(
+            examples=[
+                [f"Le patient souffre d'une {MASK_TOKEN} aiguë.", 5],
+                [f"Le médecin prescrit un traitement pour l'{MASK_TOKEN}.", 5],
+                [f"La dose recommandée est de {MASK_TOKEN} mg par jour.", 5],
+                [f"Le patient présente des symptômes de {MASK_TOKEN} chronique.", 5],
+                [f"L'examen clinique révèle une {MASK_TOKEN} au niveau du thorax.", 5],
+            ],
+            inputs=[text_input, top_k_input],
+            outputs=[predictions_output, best_pred_output],
+            fn=predict_mask,
+            cache_examples=True,
+            cache_mode="lazy",
+        )
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

requirements.txt

@@ -0,0 +1,2 @@
+transformers
+torch