Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+import torch
+
+# Load the model using Transformers' pipeline.
+print("Loading model...")
+model_pipeline = pipeline('token-classification', 'openfoodfacts/nutrition-extractor')
+print("Model loaded successfully.")
+
+def predict(image: Image.Image):
+    """
+    Receives an image, passes it directly to the nutrition extraction model,
+    and processes the token-classification output to aggregate nutritional values.
+    Assumes the model performs OCR internally.
+    """
+    # Directly pass the image to the model pipeline.
+    results = model_pipeline(image)
+    
+    # Process the output: aggregate numeric values for each entity label.
+    extracted_data = {}
+    for item in results:
+        # Expected structure: {'word': '100', 'entity': 'CALORIES', 'score': 0.98, ...}
+        label = item.get('entity', 'O').lower()
+        if label != 'o':  # Skip non-entity tokens.
+            token_text = item.get('word', '')
+            # Extract digits and decimal point.
+            num_str = "".join(filter(lambda c: c.isdigit() or c == '.', token_text))
+            try:
+                value = float(num_str)
+                extracted_data[label] = extracted_data.get(label, 0) + value
+            except ValueError:
+                continue
+
+    if not extracted_data:
+        return {"error": "No nutritional information extracted."}
+    return extracted_data
+
+# Create a Gradio interface that exposes the API.
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="json",
+    title="Nutrition Extractor API",
+    description="Upload an image of a nutrition table to extract nutritional values. The model performs OCR internally."
+)
+
+if __name__ == "__main__":
+    demo.launch()