Upload food not food text classifier demo from notebook

README.md

@@ -1,12 +1,17 @@
 ---
-title: Hf Food Not Food Text Classifier With Distilbert Demo
-emoji: 📊
-colorFrom: pink
-colorTo: purple
+title: Food Not Food Text Classifier
+emoji: 🍕🚫🍔
+colorFrom: blue
+colorTo: yellow
 sdk: gradio
 sdk_version: 5.4.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
+# 🍕🚫🍔 Food Not Food Text Classifier
+
+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)
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# 1. Import 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="adamNLP/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 outputs from 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 -- we can use markdown text to create a description field
+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)
+"""
+
+## create demo
+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 syntax error"],
+              ["A plate of waffles and bluberry syrup"]]
+)
+# 4. Launch interface -- def Main function
+if __name__ == "__main__":
+  demo.launch()

requirements.txt

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+gradio==5.4.0
+torch
+transformers