Uploading food not food text classifier demo app.py

README.md

@@ -1,12 +1,18 @@
----
-title: Food Not Food
-emoji: 📚
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 5.22.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Food Not Food Text Classifier
+emoji: 🍗🚫🥑
+colorFrom: blue
+colorTo: yellow
+sdk: gradio
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+# 🍗🚫🥑 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]:
+    # Set up text classification pipeline
+    food_not_food_classifier = pipeline(task="text-classification", 
+                                        # 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", # link to model on HF Hub
+                                        device="cuda" if torch.cuda.is_available() else "cpu",
+                                        top_k=None) # return all possible scores (not just top-1)
+    
+    # Get outputs from pipeline (as a list of dicts)
+    outputs = food_not_food_classifier(text)[0]
+
+    # 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/sreedeepEK/food-not-food/blob/main/file.ipynb).
+"""
+
+demo = gr.Interface(fn=food_not_food_classifier, 
+             inputs="text", 
+             outputs=gr.Label(num_top_classes=2), # show top 2 classes (that's all we have)
+             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 
+datasets 
+transformers
+evaluate
+accelerate