import torch
import gradio as gr

# Use a pipeline as a high-level helper
from transformers import pipeline

# model_path = "../Models/models--deepset--roberta-base-squad2/snapshots/adc3b06f79f797d1c575d5479d6f5efe54a9e3b4"

# question_answer = pipeline("question-answering", model=model_path)

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}"

# context = ("Mark Elliot Zuckerberg (/ˈzʌkərbɜːrɡ/; born May 14, 1984) is an American businessman who co-founded the social media service Facebook"
#            " and its parent company Meta Platforms, of which he is the chairman, chief executive officer, and controlling shareholder. "
#            "Zuckerberg has been the subject of multiple lawsuits regarding the creation and ownership of the website as well as issues such "
#            "as user privacy. Zuckerberg briefly attended Harvard College, where he launched Facebook in February 2004 with his roommates "
#            "Eduardo Saverin, Andrew McCollum, Dustin Moskovitz and Chris Hughes. Zuckerberg took the company public in May 2012 with majority "
#            "shares. He became the world's youngest self-made billionaire[a] in 2008, at age 23, and has consistently ranked among the world's "
#            "wealthiest individuals. According to Forbes, as of March 2025, Zuckerberg's estimated net worth stood at US$214.1 billion, "
#            "making him the second richest individual in the world,[2] behind Elon Musk and before Jeff Bezos.")
#
# question = "What is DOB of Mark?"

# answer = question_answer(question=question, context=context)
#
# print(answer)

def get_answer(file, question):
    context = read_file_content(file)
    answer = question_answer(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",lines=1)],
                    outputs=[gr.Textbox(label="Answered text",lines=4)],
                    title="@GenAILearniverse Project 5: Document Q n A",
                    description="THIS APPLICATION WILL BE USED TO ANSWER QUESTION BASED ON CONTEXT PROVIDED.")

demo.launch()