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app.py

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+import re
+import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+
+model_name = 'anugrahap/gpt2-indo-textgen'
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left')
+model = AutoModelForCausalLM.from_pretrained(model_name, pad_token_id=tokenizer.eos_token_id)
+
+generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+
+def generate(text,min_length,max_length,temperature):
+    if min_length<=max_length:
+        result = generator(text, min_length=min_length, max_length=max_length, temperature=temperature, num_return_sequences=1)
+        return result[0]["generated_text"]
+    else:
+        return "Max Length must be greater or equals to Min Length!"
+
+examples = [
+    ["Skripsi merupakan tugas akhir mahasiswa", 10, 30, 1.0],
+    ["Nama aku budi, aku adalah seorang", 20, 40, 2.0],
+    ["Indonesia adalah negara kepulauan", 30, 50, 5.0],
+]
+
+title = "GPT-2 Indonesian Text Generation"
+description = "This project is a part of thesis requirement of Anugrah Akbar Praramadhan"
+
+article = """<p style='text-align: center'> Copyright Anugrah Akbar Praramadhan 2023 <br>
+    <p style='text-align: center'> Trained on Indo4B Benchmark Dataset of Indonesian language Wikipedia with a Causal Language Modeling (CLM) objective <br>
+    <p style='text-align: center'><a href='https://huggingface.co/anugrahap/gpt2-indo-textgen' target='_blank'>Link to Trained Model</a><br>
+    <p style='text-align: center'><a href='https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf' target='_blank'>Original Paper</a>
+    """
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=[gr.inputs.Textbox(lines=5, label="Input Text"),
+            gr.Slider(label="Min Length", minimum=10, maximum=50, value=10, step=5),
+            gr.Slider(label="Max Length", minimum=20, maximum=100, value=30, step=10),
+            gr.Number(label="Temperature/Randomness (ideally 1-10)", value=1.0)],
+    outputs=gr.outputs.Textbox(label="Generated Text"),
+    examples=examples,
+    title=title,
+    description=description,
+    article=article)
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio==3.16.1
+torch==1.9.0
+transformers==4.25.1