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| import gradio as gr | |
| from transformers import pipeline | |
| # Define the models | |
| model1 = pipeline("text-classification", model="albertmartinez/bert-sdg-classification") | |
| model2 = pipeline("text-classification", model="albertmartinez/bert-multilingual-sdg-classification") | |
| model3 = pipeline("text-classification", model="albertmartinez/distilbert-multilingual-sdg-classification") | |
| model4 = pipeline("text-classification", model="albertmartinez/xlm-roberta-large-sdg-classification") | |
| def classify_text(text, model): | |
| result = model(text, top_k=16, truncation=True, max_length=512) | |
| return {p["label"]: p["score"] for p in result} | |
| def classify_all(text): | |
| return [ | |
| {p["label"]: p["score"] for p in model1(text, top_k=16, truncation=True, max_length=512)}, | |
| {p["label"]: p["score"] for p in model2(text, top_k=16, truncation=True, max_length=512)}, | |
| {p["label"]: p["score"] for p in model3(text, top_k=16, truncation=True, max_length=512)}, | |
| {p["label"]: p["score"] for p in model4(text, top_k=16, truncation=True, max_length=512)} | |
| ] | |
| ifaceall = gr.Interface( | |
| fn=classify_all, | |
| inputs=gr.Textbox(lines=2, label="Text", placeholder="Enter text here..."), | |
| outputs=[gr.Label(label="bert"), gr.Label(label="bert-multilingual"), gr.Label(label="distilbert-multilingual"), | |
| gr.Label(label="xlm-roberta-large")], | |
| title="SDG text classification", | |
| description="Enter a text and see the text classification result!", | |
| flagging_mode="never", | |
| api_name="classify_all" | |
| ) | |
| # Interface for the first model | |
| iface1 = gr.Interface( | |
| fn=lambda text: classify_text(text, model1), | |
| inputs=gr.Textbox(lines=2, label="Text", placeholder="Enter text here..."), | |
| outputs=gr.Label(label="Top SDG Predicted"), | |
| title="BERT SDG classification", | |
| description="Enter a text and see the text classification result!", | |
| flagging_mode="never", | |
| api_name="classify_bert" | |
| ) | |
| # Interface for the second model | |
| iface2 = gr.Interface( | |
| fn=lambda text: classify_text(text, model2), | |
| inputs=gr.Textbox(lines=2, label="Text", placeholder="Enter text here..."), | |
| outputs=gr.Label(label="Top SDG Predicted"), | |
| title="BERT multilingual SDG classification", | |
| description="Enter a text and see the text classification result!", | |
| flagging_mode="never", | |
| api_name="classify_bert-multilingual" | |
| ) | |
| # Interface for the three model | |
| iface3 = gr.Interface( | |
| fn=lambda text: classify_text(text, model3), | |
| inputs=gr.Textbox(lines=2, label="Text", placeholder="Enter text here..."), | |
| outputs=gr.Label(label="Top SDG Predicted"), | |
| title="DISTILBERT multilingual SDG classification", | |
| description="Enter a text and see the text classification result!", | |
| flagging_mode="never", | |
| api_name="classify_distilbert-multilingual" | |
| ) | |
| # Interface for the four model | |
| iface4 = gr.Interface( | |
| fn=lambda text: classify_text(text, model4), | |
| inputs=gr.Textbox(lines=2, label="Text", placeholder="Enter text here..."), | |
| outputs=gr.Label(label="Top SDG Predicted"), | |
| title="XLM-ROBERTA-LARGE SDG classification", | |
| description="Enter a text and see the text classification result!", | |
| flagging_mode="never", | |
| api_name="classify_xlm-roberta-large" | |
| ) | |
| with gr.Blocks() as demo: | |
| # Combine both interfaces into a tabbed interface | |
| gr.TabbedInterface( | |
| interface_list=[ifaceall, iface1, iface2, iface3, iface4], | |
| tab_names=["ALL", "bert-sdg-classification", "bert-multilingual-sdg-classification", | |
| "distilbert-multilingual-sdg-classification", "xlm-roberta-large-sdg-classification"], | |
| title="Sustainable Development Goals (SDG) Text Classifier App", | |
| theme='base' | |
| ) | |
| if __name__ == "__main__": | |
| print(gr.__version__) | |
| demo.launch() | |