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import torch | |
import gradio as gr | |
# Use a pipeline as a high-level helper | |
from transformers import pipeline | |
question_answer = pipeline( | |
"question-answering", | |
model="deepset/roberta-base-squad2") | |
def read_file_content(file_obj): | |
""" | |
Reads the content of a file object and returns it. | |
Parameters: | |
file_obj (file object): The file object to read from. | |
Returns: | |
str: The content 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"An error occurred: {e}" | |
def get_answer(file, question): | |
context = read_file_content(file) | |
answer = question_answer(question=question, context=context) | |
return answer["answer"] | |
demo = gr.Interface(fn=get_answer, inputs=[gr.File(label="File Upload"), gr.Textbox(label="Prompt Input", lines=1)], | |
outputs=[gr.Textbox(label="Response", lines=1)], | |
title="@caesar-2series: Rag Application", | |
description="Retrieval Augmented Generation Questions-Answering Application") | |
demo.launch() |