Uploading food not food text classifier demo app.py

README.md

@@ -1,12 +1,19 @@
 ---
 title: Food Not Food Text Classifier
-emoji: 🐢
-colorFrom: pink
+emoji: 🍗🚫🥑
+colorFrom: blue
 colorTo: yellow
 sdk: gradio
 sdk_version: 4.44.1
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🍗🚫🥑 Food Not Food Text Classifier
+
+Small demo to showcase a text classifier to determine if a sentence is about food or not food.
+
+DistillBERT model fine-tuned on a small synthetic dataset of 250 generated [Food or Not Food image captions](https://huggingface.co/datasets/mrdbourke/learn_hf_food_not_food_image_captions).
+
+[Source code notebook](https://github.com/mrdbourke/learn-huggingface/blob/main/notebooks/hugging_face_text_classification_tutorial.ipynb).

app.py

@@ -0,0 +1,97 @@
+# 1. Import the required packages
+import torch
+import gradio as gr
+
+from typing import Dict
+from transformers import pipeline
+
+huggingface_model_path = "Suraj-Yadav/learn_hf_food_not_food_text_classifier-distilbert-base-uncased"
+
+# 2. Define function to use our model on given text 
+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
+
+
+# 3. Create a Gradio interface with details about our app
+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",
+             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."]])
+
+# 4. Launch the interface
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+torch
+transformers