| from transformers import pipeline | |
| from langchain_huggingface import HuggingFacePipeline | |
| from langchain.prompts import PromptTemplate | |
| from transformers.utils.logging import set_verbosity_error | |
| set_verbosity_error() | |
| summarization_pipeline = pipeline("summarization", model="facebook/bart-large-cnn", device=0) | |
| summarizer = HuggingFacePipeline(pipeline=summarization_pipeline) | |
| refinement_pipeline = pipeline("summarization", model="facebook/bart-large", device=0) | |
| refiner = HuggingFacePipeline(pipeline=refinement_pipeline) | |
| qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2", device=0) | |
| summary_template = PromptTemplate.from_template("Summarize the following text in a {length} way:\n\n{text}") | |
| summarization_chain = summary_template | summarizer | refiner | |
| text_to_summarize = input("\nEnter text to summarize:\n") | |
| length = input("\nEnter the length (short/medium/long): ") | |
| summary = summarization_chain.invoke({"text": text_to_summarize, "length": length}) | |
| print("\n🔹 **Generated Summary:**") | |
| print(summary) | |
| while True: | |
| question = input("\nAsk a question about the summary (or type 'exit' to stop):\n") | |
| if question.lower() == "exit": | |
| break | |
| qa_result = qa_pipeline(question=question, context=summary) | |
| print("\n🔹 **Answer:**") | |
| print(qa_result["answer"]) |