Uploading FoodExtract demo app.py

app.py

@@ -29,23 +29,34 @@ def pred_on_text(input_text):
 # Note: You may have to replace my username `objects76` for your own
 MODEL_PATH = "objects76/FoodExtract-gemma-3-270m-fine-tune-v1"
 
-# Load the model into a pipeline
-loaded_model = AutoModelForCausalLM.from_pretrained(
-    pretrained_model_name_or_path=MODEL_PATH,
-    dtype="auto",
-    device_map="auto",
-    attn_implementation="eager"
-)
 
 # Load the tokenizer
 tokenizer = AutoTokenizer.from_pretrained(
     MODEL_PATH
 )
 
-# Create model pipeline
-loaded_model_pipeline = pipeline("text-generation",
-                                 model=loaded_model,
-                                 tokenizer=tokenizer)
+# # Load the model into a pipeline
+# loaded_model = AutoModelForCausalLM.from_pretrained(
+#     pretrained_model_name_or_path=MODEL_PATH,
+#     dtype="auto",
+#     device_map="auto",
+#     attn_implementation="eager"
+# )
+#
+# # Create model pipeline
+# loaded_model_pipeline = pipeline("text-generation",
+#                                  model=loaded_model,
+#                                  tokenizer=tokenizer)
+
+loaded_model_pipeline = pipeline(
+    "text-generation",
+    model=MODEL_PATH,  # ← pass path, let pipeline load
+    tokenizer=tokenizer,
+    torch_dtype="auto",
+    device_map="auto",
+    model_kwargs={"attn_implementation": "eager"}
+)
+
 
 # Create the demo
 description = """Extract food and drink items from text with a fine-tuned SLM (Small Language Model) or more specifically a fine-tuned [Gemma 3 270M](https://huggingface.co/google/gemma-3-270m-it).