Uploading food not food text classifier demo app.py

README.md

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 ---
-title: Learn Hf Food Not Food Text Classifier Demo
-emoji: 🦀
-colorFrom: yellow
+title: Food Not Food Text Classifier
+emoji: 🍗🚫🥑
+colorFrom: blue
 colorTo: yellow
 sdk: gradio
-sdk_version: 5.2.0
+sdk_version: 5.0.2
 app_file: app.py
-pinned: false
+pinned: true
+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/Adnan-edu/hugging_custom_ai_model).

app.py

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+
+# 1. Import the required packages
+import torch
+import gradio as gr 
+
+from typing import Dict
+from transformers import pipeline
+
+# 2. Define our function to use with our model.
+
+def set_device():
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+    elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
+        device = torch.device("mps")
+    else:
+        device = torch.device("cpu")
+    return device
+
+DEVICE = set_device()
+
+# 1. Create a function to take a String input 
+
+def food_not_food_classifier(text: str) -> Dict[str, float]:
+    # Setup food not food text classifier
+    food_not_food_classifier_pipeline = pipeline(task="text-classification",
+                                        model="mdarefin/learn_hf_food_not_food_text_classifier-distilbert-base-uncased",
+                                        batch_size=32,
+                                        device=DEVICE,
+                                        top_k=None) # top_k = None => Return all possible labels
+    
+    # Get the outputs from our pipeline
+    outputs = food_not_food_classifier_pipeline(text)[0]
+
+    # Format output from Gradio
+
+    output_dict = {}
+    for item in outputs:
+        output_dict[item["label"]] = item["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/Adnan-edu/hugging_custom_ai_model).
+"""
+
+demo = gr.Interface(fn=food_not_food_classifier, 
+             inputs="text", 
+             outputs=gr.Label(num_top_classes=2), # show top 2 classes
+             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

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+gradio==5.0.2
+torch
+transformers==4.45.2