Uploading FoodExtract-Vision demo app.py from YouTube tutorial video

README.md

@@ -1,12 +1,26 @@
 ---
-title: FoodExtract Vision V1
-emoji: 👁
-colorFrom: yellow
-colorTo: red
+title: FoodExtract-Vision Fine-tuned VLM Structued Data Extractor
+emoji: 🍟➡️📝
+colorFrom: green
+colorTo: blue
 sdk: gradio
-sdk_version: 6.5.1
 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 SmolVLM2-500M to extract food and drink items from images.
+
+Input can be any kind of image and output will be a formatted string such as the following:
+
+```json
+{'is_food': 0, 'image_title': '', 'food_items': [], 'drink_items': []}
+```
+
+Or for an image of food:
+
+```json
+{'is_food': 1, 'image_title': 'fried calamari', 'food_items': ['fried calamari'], 'drink_items': []}
+```
+
+Note: This README.md was authored in a live tutorial recorded for YouTube (link coming soon).

app.py

@@ -0,0 +1,106 @@
+import torch
+import gradio as gr
+
+import spaces
+from transformers import pipeline
+
+BASE_MODEL_ID = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct"
+# FINE_TUNED_MODEL_ID = "mrdbourke/FoodExtract-Vision-SmolVLM2-500M-fine-tune-v1"
+FINE_TUNED_MODEL_ID = "ninjals/FoodExtract-Vision-SmolVLM2-500M-fine-tune-v1-VIDEO"
+OUTPUT_TOKENS = 256
+
+# Load original base model (no fine-tuning)
+print(f"[INFO] Loading Original Model")
+original_pipeline = pipeline(
+    "image-text-to-text",
+    model=BASE_MODEL_ID,
+    dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Load fine-tuned model
+print(f"[INFO] Loading Fine-tuned Model")
+ft_pipe = pipeline(
+    "image-text-to-text",
+    model=FINE_TUNED_MODEL_ID,
+    dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+def create_message(input_image):
+    return [{'role': 'user',
+ 'content': [{'type': 'image',
+   'image': input_image},
+  {'type': 'text',
+   'text': "Classify the given input image into food or not and if edible food or drink items are present, extract those to a list. If no food/drink items are visible, return empty lists.\n\nOnly return valid JSON in the following form:\n\n```json\n{\n  'is_food': 0, # int - 0 or 1 based on whether food/drinks are present (0 = no foods visible, 1 = foods visible)\n  'image_title': '', # str - short food-related title for what foods/drinks are visible in the image, leave blank if no foods present\n  'food_items': [], # list[str] - list of visible edible food item nouns\n  'drink_items': [] # list[str] - list of visible edible drink item nouns\n}\n```\n"}]}]
+
+@spaces.GPU
+def extract_foods_from_image(input_image):
+    input_image = input_image.resize(size=(512, 512))
+    input_message = create_message(input_image=input_image)
+
+    # Get outputs from base model (not fine-tuned)
+    original_pipeline_output = original_pipeline(text=[input_message],
+                                                 max_new_tokens=OUTPUT_TOKENS)
+
+    outputs_pretrained = original_pipeline_output[0][0]["generated_text"][-1]["content"]
+
+    # Get outputs from fine-tuned model (fine-tuned on food images)
+    ft_pipe_output = ft_pipe(text=[input_message],
+                             max_new_tokens=OUTPUT_TOKENS)
+    outputs_fine_tuned = ft_pipe_output[0][0]["generated_text"][-1]["content"]
+
+    return outputs_pretrained, outputs_fine_tuned
+
+demo_title = "🥑➡️📝 FoodExtract-Vision with a fine-tuned SmolVLM2-500M"
+demo_description = """* **Base model:** https://huggingface.co/HuggingFaceTB/SmolVLM-500M-Instruct
+* **Fine-tuning dataset:** https://huggingface.co/datasets/mrdbourke/FoodExtract-1k-Vision (1k food images and 500 not food images)
+* **Fine-tuned model:** https://huggingface.co/mrdbourke/FoodExtract-Vision-SmolVLM2-500M-fine-tune-v1
+
+## Overview
+
+Extract food and drink items in a structured way from images.
+
+The original model outputs fail to capture the desired structure. But the fine-tuned model sticks to the output structure quite well.
+
+However, the fine-tuned model could definitely be improved with respects to its ability to extract the right food/drink items.
+
+Both models use the input prompt:
+
+````
+Classify the given input image into food or not and if edible food or drink items are present, extract those to a list. If no food/drink items are visible, return empty lists.
+
+Only return valid JSON in the following form:
+
+```json
+{
+  'is_food': 0, # int - 0 or 1 based on whether food/drinks are present (0 = no foods visible, 1 = foods visible)
+  'image_title': '', # str - short food-related title for what foods/drinks are visible in the image, leave blank if no foods present
+  'food_items': [], # list[str] - list of visible edible food item nouns
+  'drink_items': [] # list[str] - list of visible edible drink item nouns
+}
+```
+````
+
+Except one model has been fine-tuned on the structured data whereas the other hasn't.
+
+Notable next steps would be:
+* **Remove the input prompt:** Just train the model to go straight from image -> text (no text prompt on input), this would save on inference tokens.
+* **Fine-tune on more real-world data:** Right now the model is only trained on 1k food images (from Food101) and 500 not food (random internet images), training on real world data would likely significantly improve performance.
+* **Fix the repetitive generation:** The model can sometimes get stuck in a repetitive generation pattern, e.g. "onions", "onions", "onions", etc. We could look into patterns to help reduce this.
+"""
+
+demo = gr.Interface(
+    fn=extract_foods_from_image,
+    inputs=gr.Image(type="pil"),
+    title=demo_title,
+    description=demo_description,
+    outputs=[gr.Textbox(lines=4, label="Original Model (not fine-tuned)"),
+             gr.Textbox(lines=4, label="Fine-tuned Model")],
+    examples=[["examples/camera.jpeg"],
+              ["examples/Tandoori-Chicken.jpg"],
+              ["examples/fries.jpeg"]],
+)
+
+if __name__ == "__main__":
+    demo.launch(share=False)

requirements.txt

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+num2words
+transformers
+torch
+accelerate
+gradio
+torchvision