Uploading food not food text classifier demo app.py

README.md

@@ -1,12 +1,18 @@
 ---
-title: Learn Hf Food Not Food Text Classifier Demo
-emoji: 🏢
-colorFrom: yellow
-colorTo: gray
+title: Food not Food Text Classifier app
+emoji: 🍗🚫🥑
+colorFrom: blue
+colorTo: red
 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. (Completed the course on 4/10/2024)
+
+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

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+
+import torch
+import gradio as gr
+
+from typing import TypedDict
+from transformers import pipeline
+
+# Create function to use our model on given text
+def food_not_food_classifier(text: str) -> Dict[str, float]:
+    food_not_food_classifier = pipeline(task="text-classifcation",
+                                         # Because our model is on Hugging Face already, we can pass in the model name directly
+                                        model="mrdbourke/learn_hf_food_not_food_text_classifier-distilbert-base-uncased",
+                                        device="cuda" if torch.cuda.is_available() else "cpu",
+                                        top_k=None
+                                        )
+    outputs = food_not_food_classifier(text)[0]
+
+    output_dict = {}
+    for item in outputs:
+        output_dict[item["label"]] = item["score"]
+
+    return output_dict
+
+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=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 seems to have a syntax error."],
+                              ["A delicious photo of a plate of scrambled eggs, bacon and toast."]])
+
+if __name__ == "__main__":
+  demo.launch()

requirements.txt

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+gradio
+torch
+transformers