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| import gradio as gr | |
| from transformers import pipeline | |
| # Load lightweight QA model | |
| qa_pipeline = pipeline( | |
| "question-answering", | |
| model="distilbert-base-cased-distilled-squad" | |
| ) | |
| # Function | |
| def answer(context, question): | |
| if context.strip() == "" or question.strip() == "": | |
| return "Please enter context and question." | |
| result = qa_pipeline( | |
| question=question, | |
| context=context | |
| ) | |
| return result["answer"] | |
| # UI | |
| demo = gr.Interface( | |
| fn=answer, | |
| inputs=[ | |
| gr.Textbox(lines=8, label="Context"), | |
| gr.Textbox(lines=2, label="Question") | |
| ], | |
| outputs="text", | |
| title="๐ RAG-QA System", | |
| description="Ask questions from given context.", | |
| theme=gr.themes.Soft() | |
| ) | |
| demo.launch(server_name="0.0.0.0", server_port=7860) |