Update app.py

app.py

@@ -16,29 +16,30 @@ model_path = hf_hub_download(
 # Cargar el modelo usando la ruta absoluta
 model = joblib.load(model_path)
 
-def predict_text(title, abstract):
-    text = f"{title} {abstract}"
-    pred = model.predict([text])[0]
-    return pred
+# --- Funciones ---
+def predict_single(text):
+    return model.predict([text])[0]
 
-def predict_file(file):
-    df = pd.read_csv(file, sep=";")
-    df["Prediction"] = model.predict(df["title"] + " " + df["abstract"])
-    return df
+def predict_batch(texts):
+    return [model.predict([t])[0] for t in texts]
 
+# --- Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# 🩺 Medical Text Classifier")
-
-    with gr.Tab("Texto individual"):
-        title = gr.Textbox(label="Título")
-        abstract = gr.Textbox(label="Abstract")
-        output = gr.Textbox(label="Predicción")
-        btn = gr.Button("Clasificar")
-        btn.click(predict_text, inputs=[title, abstract], outputs=output)
-
-    with gr.Tab("Archivo CSV"):
-        file_input = gr.File(label="Subir CSV", file_types=[".csv"])
-        file_output = gr.Dataframe()
-        file_input.change(predict_file, inputs=file_input, outputs=file_output)
-
-demo.launch()
+    # Endpoint single prediction
+    single_input = gr.Textbox(label="Input text")
+    single_output = gr.Textbox(label="Predicted category")
+    single_btn = gr.Button("Predict")
+    single_btn.click(predict_single, inputs=single_input, outputs=single_output)
+
+    # Endpoint batch prediction
+    batch_input = gr.Textbox(label="Batch texts (comma separated)")
+    batch_output = gr.Textbox(label="Predictions (comma separated)")
+    batch_btn = gr.Button("Predict Batch")
+    batch_btn.click(
+        lambda x: predict_batch(x.split(",")),
+        inputs=batch_input,
+        outputs=batch_output
+    )
+
+# --- Lanzar en API mode ---
+demo.launch(server_name="0.0.0.0", server_port=7860, api_mode=True)