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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="devagonal/bert-f1-durga-muhammad-c", # 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 question class. | |
| label0 = durga | |
| label1 = muhammad | |
| """ | |
| 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="Bert F1 Durga Muhammad c", | |
| description=description, | |
| examples=[["siapakah durga"], | |
| ["siapakah muhammad"]]) | |
| # 4. Launch the interface | |
| if __name__ == "__main__": | |
| demo.launch() | |