| | |
| | from transformers import pipeline |
| | import torch |
| | import gradio as gr |
| |
|
| | question_answering = pipeline("question-answering", model="deepset/roberta-base-squad2") |
| |
|
| | |
| |
|
| | |
| |
|
| | def read_file(file_obj): |
| | """ |
| | Reads the contents of a given file object. |
| | |
| | Args: |
| | file_obj (file): The file object to be read. |
| | |
| | Returns: |
| | str: The contents of the file. |
| | """ |
| | try: |
| | with open(file_obj.name, 'r', encoding='utf-8') as file: |
| | context = file.read() |
| | return context |
| | except Exception as e: |
| | return f"Error: Unable to read the file. {e}" |
| |
|
| | def get_answer(file_obj, question): |
| | context = read_file(file_obj=file_obj) |
| | answer = question_answering(question=question, context=context) |
| | return answer["answer"] |
| |
|
| | gr.close_all() |
| |
|
| | demo = gr.Interface(fn=get_answer, |
| | inputs=[gr.File(label="Upload your file" ), gr.Textbox(label="Input your question here...", lines=2 )], |
| | outputs=[gr.Textbox(label="Answer Text", lines=2)], |
| | title="@IT AI Enthusiast (https://www.youtube.com/@itaienthusiast/) - Project 5: DocuQ&A", |
| | description="This application will be used to Ask Questions Based On The Context Given To It", |
| | concurrency_limit=16) |
| | demo.launch() |
| |
|
| |
|
