Initial commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,6 +1,6 @@
 ---
 title: Ingredients Spellcheck
-emoji: 🦀
+emoji: 🍊
 colorFrom: green
 colorTo: indigo
 sdk: gradio

demo.py

@@ -0,0 +1,108 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import spaces
+
+
+# Example images and texts
+EXAMPLES = [
+    ["images/ingredients_1.jpg", "24.36% chocolat noir 63% origine non UE (cacao, sucre, beurre de cacao, émulsifiant léci - thine de colza, vanille bourbon gousse), œuf, farine de blé, beurre, sucre, miel, sucre perlé, levure chimique, zeste de citron."],
+    ["images/ingredients_2.jpg", "farine de froment, œufs, lait entier pasteurisé Aprigine: France), sucre, sel, extrait de vanille naturelle Conditi( 35."],
+    ["images/ingredients_3.jpg", "tural basmati rice - cooked (98%), rice bran oil, salt"],
+    ["images/ingredients_4.jpg", "Eau de noix de coco 93.9%, Arôme natutel de fruit"],
+    ["images/ingredients_5.jpg", "Sucre, pâte de cacao, beurre de cacao, émulsifiant: léci - thines (soja). Peut contenir des traces de lait. Chocolat noir: cacao: 50% minimum. À conserver à l'abri de la chaleur et de l'humidité. Élaboré en France."],
+]
+MODEL_ID = "openfoodfacts/spellcheck-mistral-7b"
+
+
+# CPU/GPU device
+zero = torch.Tensor([0]).cuda()
+
+# Tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.pad_token_id = tokenizer.eos_token_id
+
+# Model
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    device_map="auto",
+    attn_implementation="flash_attention_2",
+    torch_dtype=torch.bfloat16,
+)
+
+@spaces.GPU
+def process(text: str) -> str:
+    """Take the text, the tokenizer and the causal model and generate the correction."""
+    prompt = prepare_instruction(text)
+    input_ids = tokenizer(
+        prompt, 
+        add_special_tokens=True, 
+        return_tensors="pt"
+    ).input_ids
+    output = model.generate(
+        input_ids.to(zero.device), # GPU
+        do_sample=False,
+        max_new_tokens=512,
+    )
+    return tokenizer.decode(output[0], skip_special_tokens=True)[len(prompt):].strip()
+
+
+def prepare_instruction(text: str) -> str:
+    """Prepare instruction prompt for fine-tuning and inference.
+
+    Args:
+        text (str): List of ingredients
+
+    Returns:
+        str: Instruction.
+    """
+    instruction = (
+        "###Correct the list of ingredients:\n"
+        + text
+        + "\n\n###Correction:\n"
+    )
+    return instruction
+
+
+##########################
+# GRADIO SETUP
+##########################
+
+# Creating the Gradio interface
+with gr.Blocks() as demo:
+
+    gr.Markdown("# Ingredients Spellcheck")
+    gr.Markdown("")
+    
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(type="pil", label="image_input")
+            ingredients = gr.Textbox(label="List of ingredients")
+            spellcheck_button = gr.Button(value='Spellcheck')
+
+        with gr.Column():
+            correction = gr.Textbox(label="Correction", interactive=False)
+
+    with gr.Row():
+        gr.Examples(
+            fn=process,
+            examples=EXAMPLES,
+            inputs=[
+                image,
+                ingredients,
+            ],
+            outputs=[correction],
+            run_on_click=False,
+        )
+
+    spellcheck_button.click(
+        fn=process,
+        inputs=[ingredients],
+        outputs=[correction]
+    )
+
+
+if __name__ == "__main__":
+    # Launch the demo
+    demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+gradio
+flash-attn
+transformers
+spaces
+sentencepiece