Uploading food not food text classifier demo app

README.md

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----
-title: Food Not Food Text Classifier App
-emoji: 📉
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.44.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import os
+from demos.food_not_food_text_classifier.app_utility import gradio_food_classifier
+
+description = """
+A text classifier to determine if a sentence is about food or not food.
+
+Fine-tuned from [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) on a [small dataset of food and not food text](https://huggingface.co/datasets/mrdbourke/learn_hf_food_not_food_image_captions).
+
+See [source code](https://github.com/mrdbourke/learn-huggingface/blob/main/notebooks/hugging_face_text_classification_tutorial.ipynb).
+"""
+
+demo = gr.Interface(fn=gradio_food_classifier,
+             inputs="text",
+             outputs=gr.Label(num_top_classes=2),
+             title="🍗🚫🥑 Food or Not Food Text Classifier App",
+             description=description,
+             examples=[["I whipped up a fresh batch of code, but it seems to have a syntax error."],
+                       ["A delicious photo of a plate of scrambled eggs, bacon and toast."]])
+
+
+if __name__ == "__main__":
+    demo.launch()

app_utility.py

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+import torch
+from typing import Union,Dict
+from transformers import pipeline
+from constants import HUGGINGFACE_MODEL_PATH
+
+
+def set_device() -> torch.device:
+    """
+    Set the device to the best available option: CUDA (if available), MPS (if available on Mac),
+    or CPU as a fallback. Provides a robust selection mechanism for production environments.
+
+    Returns:
+        torch.device: The best available device for computation.
+    """
+    if torch.cuda.is_available():
+        return torch.device("cuda")
+    elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
+        return torch.device("mps")
+    else:
+        return torch.device("cpu")
+
+
+def food_not_food_classifier(
+    text: Union[str, list],
+    model_path: str,
+    batch_size: int = 32,
+    device: str = None,
+    get_classifier:bool = False
+) -> Dict[str, float]:
+    """
+    Classifies whether the given text is related to food or not, returning a dictionary of labels and their scores.
+
+    Args:
+        text (Union[str, list]): The input text or list of texts to classify.
+        model_path (str): The path to the Hugging Face model for classification.
+        batch_size (int): The batch size for processing. Default is 32.
+        device (str): The device to run inference on (e.g., 'cuda', 'cpu'). Default is None (auto-detect best available).
+
+    Returns:
+        Dict[str, float]: A dictionary where the keys are the labels and the values are the classification scores.
+    """
+
+    if device is None:
+        device = set_device()
+
+    classifier = pipeline(
+        task="text-classification",
+        model=model_path,
+        batch_size=batch_size,
+        device=device,
+        top_k=None  # Keep all predictions
+    )
+
+    if get_classifier:
+      return classifier
+    else:
+
+      results = classifier(text)    # [[{'label': 'food', 'score': 0.9500328898429871}, {'label': 'not_food', 'score': 0.04996709153056145}]]
+
+      output_dict = {}
+      for output in results[0]:
+          output_dict[output['label']] = output['score']
+
+      return output_dict
+
+
+def gradio_food_classifier(text: str) -> dict:
+    """
+    A wrapper function for Gradio to classify text using the classify_food_text function.
+
+    Args:
+        text (str): The input text to classify.
+
+    Returns:
+        dict: Classification results as a dictionary of label and score.
+    """
+    classifier = food_not_food_classifier(text=text,
+                                          model_path=HUGGINGFACE_MODEL_PATH,
+                                          get_classifier=True)
+
+    results = classifier(text)
+
+    output_dict = {}
+    for output in results[0]:
+        output_dict[output['label']] = output['score']
+
+    return output_dict

constants.py

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+HUGGINGFACE_MODEL_PATH = "Suraj-Yadav/learn_hf_food_not_food_text_classifier-distilbert-base-uncased"
+

requirements.txt

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+gradio
+torch
+transformers