| import gradio as gr | |
| from transformers import pipeline | |
| model_id = "knkarthick/TOPIC-DIALOGSUM" | |
| generator = pipeline(task="text2text-generation", model=model_id) | |
| def split_paragraph(paragraph, max_chunk_size=1024): | |
| words = paragraph.split() | |
| chunks = [] | |
| current_chunk = [] | |
| for word in words: | |
| if len(current_chunk) + len(word) + 1 <= max_chunk_size: | |
| current_chunk.append(word) | |
| else: | |
| chunks.append(' '.join(current_chunk)) | |
| current_chunk = [word] | |
| if current_chunk: | |
| chunks.append(' '.join(current_chunk)) | |
| return chunks | |
| def launch(input): | |
| if len(input) > 1024: | |
| return " ".join([res["generated_text"] for res in generator(split_paragraph(input, 1024))]) | |
| return generator(input)[0]["generated_text"] | |
| iface = gr.Interface(launch, inputs="text", outputs="text") | |
| iface.launch() |