Uploading food classifier demo

README.md

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----
-title: MyWorkSS
-emoji: 📊
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 6.3.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: Testing pilots
----
-
-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
+sdk_version: 4.36.1
+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.
+
+library_name: transformers
+license: apache-2.0
+base_model: distilbert/distilbert-base-uncased
+tags:
+  - generated_from_trainer
+metrics:
+  - accuracy
+model-index:
+  - name: learn_hf_food_not_food_text_classfier-distilbert-base-uncased
+    results: []

Requirements.txt

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+# Requirements 🍗🚫🥑 Food Not Food Text Classifier
+gradio
+torch
+transformers

app.py

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+# Lets Launch Sagar's demo on web
+#  YAML metadata looks for app.py by default
+# app.py import packages , define gradio function, create demo, run demo with demo.launch 
+# %%writefile  magic pyhton function 
+# Create Model Demo: Gradio App: inputs - model -output in gradio app
+# Create a function to perform infeence 
+# 1  take an input of strings 
+# 2 setup a text classsifcation pipeline 
+# 3 get the output from piepeline return theoutput from the pipeline in step3 as a formatted dictionary with format:
+# 4 Return the output from the pipeline in step 3 as a formatted dictionary with the format {"label_1": probability_1, "label_2: probability_2"}
+######################################################################################################################
+# Import necessary packages
+import pprint
+from pathlib import Path
+
+import numpy as np
+import torch
+
+import datasets
+import evaluate
+from typing import Dict, List
+from transformers import pipeline
+from transformers import TrainingArguments, Trainer
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+
+# 1. Provide Hugging face model path copy from hugging face
+
+food_not_food_pipeline = pipeline(
+    task="text-classification",  
+    model="Quantum-Monk/learn_hf_food_not_food_text_classfier-distilbert-base-uncased",
+    batch_size=32,
+    # Use 0 for the first GPU or "cpu"
+    device=0 if torch.cuda.is_available() else -1, 
+    top_k=None # None returns all possible labels
+)
+
+# 2. Create the function to use the pipeline
+def food_not_food_classifier(text: str) -> List[Dict[str, float]]:
+    # 3. Get the outputs from our pipeline 
+    # The pipeline returns a list of results
+    outputs = food_not_food_pipeline(text)[0]
+    return outputs 
+
+# 4. Test the function
+result = food_not_food_classifier(text="Yo we're building a local demo!")
+print(result)
+
+#format output for gradio
+import gradio as gr
+
+# 1. Update your classifier function to include the formatting logic
+def food_not_food_classifier(text: str):
+    # Get the raw output from your pipeline (already defined earlier)
+    # This returns a list of dictionaries because top_k=None
+    raw_outputs = food_not_food_pipeline(text)[0]
+
+    # 2. Format the output specifically for Gradio Label component
+    output_dict = {}
+    for item in raw_outputs:
+        output_dict[item["label"]] = item["score"]
+
+    return output_dict
+
+# 3. Create the Gradio interface
+
+desciption = "A text classfier to determine if a sentence is about food or not food fine tuner from distilbert HF model and dataset. my personal repo located at https://huggingface.co/Quantum-Monk/learn_hf_food_not_food_text_classfier-distilbert-base-uncased my space https://huggingface.co/spaces/Quantum-Monk/MyWorkSS "
+
+demo = gr.Interface(
+    fn=food_not_food_classifier,
+    inputs="text",
+    outputs=gr.Label(num_top_classes=2),
+    title="Food Not Food Classifier",
+    description="A text classifier to determine if a sentence is about food or not.",
+    examples=[
+        ["I whipped up a fresh batch of code, but it seems to have a syntax error"],
+        ["A pancake plate of ice cream"]
+    ]
+)
+
+
+# Make directory to store our demo
+
+from pathlib import Path
+# Make Directory
+demos_dir = Path("../demos")
+demos_dir.mkdir(exist_ok=True)
+
+# Create a folder for food_not_food_text_classfier demo
+
+food_not_food_text_classifier_demo_dir = Path(demos_dir, "food_not_food_text_classifier")
+food_not_food_text_classifier_demo_dir.mkdir(exist_ok=True)
+
+# 4. Sagar HF Launch interface!
+
+if __name__ == "__main__":
+    demo.launch()
+
+
+