Uploading our food not food text classifier demo from the video!

README.md

@@ -1,12 +1,19 @@
 ---
-title: Learn Hf Food Not Food Text Classifier Demo Video
-emoji: 🏃
+title: Food Not Food Text Classifier
+emoji: 🍗🚫🥑
 colorFrom: blue
-colorTo: blue
+colorTo: yellow
 sdk: gradio
 sdk_version: 4.40.0
 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.
+
+DistilBERT model fine-tuned on a small synthetic dataset of [250 generated food/not_food image captions](https://huggingface.co/datasets/mrdbourke/learn_hf_food_not_food_image_captions). 
+
+See [source code notebook](https://github.com/mrdbourke/learn-huggingface/blob/main/notebooks/hugging_face_text_classification_tutorial.ipynb).

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 food_not_food_classifier(text: str) -> Dict[str, float]:
+  # 2. Setup food not food text classifier
+  food_not_food_classifier_pipeline = pipeline(task="text-classification",
+                                                model="mrdbourke/learn_hf_food_not_food_text_classifier-distilbert-base-uncased",
+                                                batch_size=32,
+                                                device="cuda" if torch.cuda.is_available() else "cpu",
+                                                top_k=None) # top_k=None => return all possible labels
+
+  # 3. Get the outputs from our pipeline
+  outputs = food_not_food_classifier_pipeline(text)[0]
+
+  # 4. Format output for Gradio
+  output_dict = {}
+  for item in outputs:
+    output_dict[item["label"]] = item["score"]
+
+  return output_dict
+
+# 3. Create a Gradio interface
+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) a [dataset of LLM generated food/not_food image captions](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=food_not_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 to seems to have a syntax error"],
+              ["A plate of pancakes and strawberry icing"]]
+)
+
+# 4. Launch the interface
+if __name__ == "__main__":
+  demo.launch()

requirements.txt

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+gradio
+torch
+transformers