Add application

app.py

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+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+
+large_model_name = "DmitryMalikov/t5-base-question-gen"
+small_model_name = "DmitryMalikov/t5-small-question-gen"
+
+tokenizers = {
+    "Base T5": AutoTokenizer.from_pretrained(large_model_name),
+    "Small T5": AutoTokenizer.from_pretrained(small_model_name),
+}
+models = {
+    "Base T5": AutoModelForSeq2SeqLM.from_pretrained(large_model_name),
+    "Small T5": AutoModelForSeq2SeqLM.from_pretrained(small_model_name),
+}
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+for model in models.values():
+    model.to(device)
+
+
+def generate_question(context, model_choice):
+    tokenizer = tokenizers[model_choice]
+    model = models[model_choice]
+
+    input_text = context.strip()
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=256).to(device)
+
+    outputs = model.generate(
+        **inputs,
+        max_length=64,
+        num_beams=4,
+        early_stopping=True
+    )
+    question = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return question
+
+
+iface = gr.Interface(
+    fn=generate_question,
+    inputs=[
+        gr.Textbox(label="Context", lines=5, placeholder="Enter text context for question..."),
+        gr.Dropdown(choices=["Base T5", "Small T5"], label="Choose model", value="Small T5")
+    ],
+    outputs=gr.Textbox(label="Generated question"),
+    title="Question generation based on context",
+    description="Enter text and receive question that can be answered with given context."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+

requirements.txt

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+transformers>=4.0.0
+torch
+gradio