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: green
-colorTo: indigo
+title: Food Not Food Text Classifier
+emoji: 😋🙅🥑
+colorFrom: blue
+colorTo: yellow
 sdk: gradio
-sdk_version: 5.6.0
 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.
+
+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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+# 1. Import the required packages
+import torch
+import gradio as gr
+
+from typing import Dict
+from transformers import pipeline
+
+# 2. Define function to use our model on given text 
+def food_not_food_classifier(text: str) -> Dict[str, float]:
+    """
+    Takes an input string of text and classifies it into food/not_food in the form of a dictionary.
+    """
+    # 2. Setup the pipeline to use the local model (or Hugging Face model path)
+    food_not_food_classifier = pipeline(task='text-classification',
+                                        model = "shivajimallela/learn_hf_food_not_food_classifier-ditsilbert-base-uncased",
+                                        device= "cuda" if torch.cuda.is_available() else "cpu",
+                                        top_k = None)
+    
+    # 3. Get outputs from pipeline (as a list of dicts)
+    outputs = food_not_food_classifier(text)[0]
+    # print(food_not_food_classifier(text))
+
+    # 4. Format output for Gradio (e.g. {"label_1": probability_1, "label_2": probability_2})
+    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/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."]])
+
+# 4. Launch the interface
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+torch
+transformers