feat: uploading food caption classifier model

README.md

@@ -1,12 +1,17 @@
 ---
-title: Learn Hf Food Caption Text Classifier
-emoji: 🌖
-colorFrom: green
-colorTo: pink
+title: Food Caption Classifier
+emoji: 🍨🍱
+colorFrom: blue
+colorTo: purple
 sdk: gradio
 sdk_version: 5.1.0
 app_file: app.py
-pinned: false
+pinned: true
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🍨🍱 Food Caption Classifier
+
+A text classifier model to determine whether a caption 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 captions](https://huggingface.co/datasets/mrdbourke/learn_hf_food_not_food_image_captions)

app.py

@@ -0,0 +1,46 @@
+import torch
+import gradio as gr
+from transformers import pipeline
+
+from typing import Dict
+
+
+def food_not_food_classifier(text: str) -> Dict[str, float]:
+    # Create the classifier pipeline
+    food_not_food_classifier_pipeline = pipeline(
+        task="text-classification",
+        model="joadithya/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  # Returning all possible labels for a given input
+    )
+
+    # Get the outputs from the pipeline
+    outputs = food_not_food_classifier_pipeline(text)[0]
+
+    # Format output for Gradio
+    output_dict = {}
+    for item in outputs:
+        output_dict[item["label"]] = item["score"]
+
+    return output_dict
+
+
+description = """
+A text classifier model to determine whether a caption 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 captions](https://huggingface.co/datasets/mrdbourke/learn_hf_food_not_food_image_captions)
+"""
+
+demo = gr.Interface(
+    inputs="text",
+    outputs=gr.Label(num_top_classes=2),
+    title="Food Caption Classifier",
+    description=description,
+    examples=[["Nothing beats the taste of home"],
+              ["Love served on a plate"],
+              ["A toast with cherry on top"]] 
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+torch
+transformers