New: Do actual prediction

app.py

@@ -3,6 +3,7 @@ import os        # Umgebungsvariablen (z.B. HF_TOKEN)
 import types     # für Instanz-Monkeypatch (fastText .predict)
 import html      # HTML-Escaping für Ausgabe/Gradio
 import numpy as np  # Numerik (z.B. für Wahrscheinlichkeiten)
+import time
 
 # Machine Learning / NLP
 import torch                      # PyTorch (Model, Tensor, Device)
@@ -13,6 +14,7 @@ import sentencepiece              # Required für SentencePiece-basierte Tokeniz
 import tiktoken                   # Optionaler Converter; verhindert Fallback-Fehler/Warnungen
 
 # Hugging Face / Ökosystem
+import spaces
 from transformers import AutoTokenizer   # Tokenizer-Lader (mit use_fast=False für SentencePiece)
 from huggingface_hub import hf_hub_download  # Dateien/Weights aus dem HF Hub laden
 from safetensors.torch import load_file      # Sicheres & schnelles Laden von Weights (.safetensors)
@@ -110,16 +112,24 @@ def is_eng(review: str):
     return lang_label[1] == "__label__en", lang_prob
 
 ### Do actual prediction #########################################################
-
+@spaces.GPU(duration=120)  # Sekunden GPU-Zeit pro Call
 def predict(review: str, mode: str):
 
     review = (review or "").strip()
 
-    review_is_eng, review_is_eng_prob = is_eng(review)
-
     if not review:
         # immer zwei Outputs zurückgeben
         return "<i>Please enter a review.</i>", {}
+    
+    prediction_flavours = {}
+
+    review_is_eng, review_is_eng_prob = is_eng(review)
+
+    if review_is_eng:
+        # Do actual predictions if is english and whisky note
+        t_start_flavours = time.time()
+        prediction_flavours = predict_flavours(review, model_flavours, tokenizer_flavours, device)
+        t_end_flavours = time.time()
 
     if mode == "table":
         html_out = f"<b>{html.escape(review)}</b> | <i>{mode}</i>"
@@ -130,11 +140,21 @@ def predict(review: str, mode: str):
         json_out = {
             "review": review,
             "mode": mode,
+            'model': FILENAME,
             "device": device,
+            "duration": round((t_end_flavours - t_start_flavours), 3),
             "is_en": {
                 "is": review_is_eng,
                 "prob": review_is_eng_prob
-            }
+            },
+            "results": [{
+                    'icon': ICONS[name],
+                    'name': name,
+                    'score': score,
+                    #'level': get_level(score)
+                }
+                for name, score in prediction_flavours.items()
+            ],
         }
         return html_out, json_out
     

requirements.txt

@@ -6,4 +6,5 @@ huggingface_hub
 safetensors
 sentencepiece
 tiktoken
-accelerate>=0.30
+accelerate>=0.30
+spaces