Add application file

app.py

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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="",
+    description="Enter a text and see the text classification result!"
+)
+
+# 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="albertmartinez/bert-sdg-classification",
+    description="Enter a text and see the text classification result!"
+)
+
+# 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="albertmartinez/bert-multilingual-sdg-classification",
+    description="Enter a text and see the text classification result!"
+)
+
+# 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="albertmartinez/distilbert-multilingual-sdg-classification",
+    description="Enter a text and see the text classification result!"
+)
+
+# 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="albertmartinez/xlm-roberta-large-sdg-classification",
+    description="Enter a text and see the text classification result!"
+)
+
+# Combine both interfaces into a tabbed interface
+io = 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'
+)
+
+io.launch()

requirements.txt

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+transformers
+torch