Update app

app.py

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+import gradio as gr
+from transformers import T5ForConditionalGeneration, T5TokenizerFast
+import nltk
+from nltk import tokenize
+
+checkpoint = "yhavinga/t5-base-dutch"
+tokenizer = T5TokenizerFast.from_pretrained(checkpoint)
+tokenizer.sep_token = '<sep>'
+tokenizer.add_tokens(['<sep>'])
+
+hfmodel = T5ForConditionalGeneration.from_pretrained("Michelvh/t5-end2end-questions-generation-dutch")
+
+def hf_run_model(input_string, **generator_args):
+    generator_args = {
+    "max_length": 256,
+    "num_beams": 4,
+    "length_penalty": 1.5,
+    "no_repeat_ngram_size": 3,
+    "early_stopping": True,
+    "num_return_sequences": 1,
+    }
+    input_string = "generate questions: " + input_string + " </s>"
+    input_ids = tokenizer.encode(input_string, return_tensors="pt")
+    res = hfmodel.generate(input_ids, **generator_args)
+    output = tokenizer.batch_decode(res, skip_special_tokens=True)
+    output = [item.split("<sep>") for item in output]
+    return output
+
+
+def chunkText(text, frameSize=5):
+    sentences = tokenize.sent_tokenize(text)
+    frames = []
+    step_size = frameSize - 1
+    for index in range(len(sentences) - step_size + 1):
+        frames.append(" ".join(sentences[index:index + step_size]))
+    return frames
+
+
+def flatten(l):
+    return [item for sublist in l for item in sublist]
+
+
+def run_model_with_frames(text):
+    frames = chunkText(text)
+    result = set()
+    for frame in frames:
+        answers = flatten(hf_run_model(frame))
+        for answer in answers:
+            result.add(answer.strip())
+    return result
+
+
+iface = gr.Interface(fn=run_model_with_frames, inputs="text", outputs="text")
+iface.launch()

requirements.txt

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+transformers
+nltk