Uploading FoodExtract demo app.py

README.md

@@ -1,12 +1,36 @@
 ---
-title: FoodExtract V1
-emoji: 💻
-colorFrom: red
-colorTo: green
+title: FoodExtract Fine-tuned LLM Structued Data Extractor
+emoji: 📝➡️🍟
+colorFrom: green
+colorTo: blue
 sdk: gradio
-sdk_version: 6.2.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+"""
+Fine-tuned Gemma 3 270M to extract food and drink items from raw text.
+
+Input can be any form of real text and output will be a formatted string such as the following:
+
+```
+food_or_drink: 1
+tags: fi, re
+foods: tacos, milk, red apple, pineapple, cherries, fried chicken, steak, mayonnaise
+drinks: iced latte, matcha latte
+```
+
+The tags map to the following items:
+
+```
+tags_dict = {'np': 'nutrition_panel',
+ 'il': 'ingredient list',
+ 'me': 'menu',
+ 're': 'recipe',
+ 'fi': 'food_items',
+ 'di': 'drink_items',
+ 'fa': 'food_advertistment',
+ 'fp': 'food_packaging'}
+```
+"""

app.py

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+
+# Load dependencies
+import time
+import transformers
+import torch
+
+import gradio as gr
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import pipeline
+
+def pred_on_text(input_text):
+    start_time = time.time()
+
+    raw_output = loaded_model_pipeline(text_inputs=[{"role": "user",
+                                                    "content": input_text}],
+                                       max_new_tokens=256,
+                                       disable_compile=True)
+    end_time = time.time()
+    total_time = round(end_time - start_time, 4)
+
+    generated_text = raw_output[0]["generated_text"][1]["content"]
+
+    return generated_text, raw_output, total_time
+
+# Load the model (from our Hugging Face Repo)
+MODEL_PATH = "mrdbourke/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"
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    MODEL_PATH
+)
+
+# Create model pipeline
+loaded_model_pipeline = pipeline("text-generation",
+                                 model=loaded_model,
+                                 tokenizer=tokenizer)
+
+# Create the demo
+description = """Extract food and drink items from text with a fine-tuned SLM (Small Language Model).
+
+* Input (str): Raw text strings or image captions (e.g. "A photo of a dog sitting on a beach" or "A breakfast plate with bacon, eggs and toast")
+* Output (str): Generated text with food/not_food classification as well as noun extracted food and drink items and various food tags.
+
+For example:
+
+Input: "For breakfast I had eggs, bacon and toast and a glass of orange juice"
+Output: 
+food_or_drink: 1
+tags: fi, di
+foods: eggs, bacon, toast
+drinks: orange juice
+"""
+
+demo = gr.Interface(fn=pred_on_text,
+                    inputs=gr.TextArea(lines=4, label="Input Text"),
+                    outputs=[gr.TextArea(lines=4, label="Generated Text"),
+                             gr.TextArea(lines=7, label="Raw Output"),
+                             gr.Number(label="Generation Time (s)")],
+                    title="🍳 Structured FoodExtract with a Fine-Tuned Gemma 3 270M",
+                    description=description,
+                    examples=[["Hello world! This is my first fine-tuned LLM!"],
+                              ["A plate of food with grilled barramundi, salad with avocado, olives, tomatoes and Italian dressing"],
+                              ["British Breakfast with baked beans, fried eggs, black pudding, sausages, bacon, mushrooms, a cup of tea and toast and fried tomatoes"],
+                              ["Steak tacos"],
+                              ["A photo of a dog sitting on a beach"]]
+)
+
+if __name__ == "__main__":
+    demo.launch(share=False)

requirements.txt

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+transformers
+gradio
+torch