app.py

import streamlit as st
import torch
from transformers import DistilBertTokenizer, DistilBertForQuestionAnswering

def find_answer(question, context, model, tokenizer):
    # Tokenize the input question and context
    inputs = tokenizer.encode_plus(question, context, add_special_tokens=True, return_tensors="pt", max_length=512, truncation=True)
    input_ids = inputs["input_ids"].tolist()[0]

    # Get the logits for the start and end positions
    with torch.no_grad():
        output = model(**inputs)

    start_logits = output['start_logits']
    end_logits = output['end_logits']

    # Find the start and end positions with the highest probabilities
    start_idx = torch.argmax(start_logits)
    end_idx = torch.argmax(end_logits)

    # Get the answer from the original context using the tokens between start and end positions
    answer = tokenizer.decode(input_ids[start_idx:end_idx+1], skip_special_tokens=True)
    return answer

def read_file(file_path):
    with open(file_path, "rb") as f:
        raw_data = f.read()
        result = chardet.detect(raw_data)
        encoding = result['encoding']
    with open(file_path, "r", encoding=encoding) as file:
        context = file.read()
    return context

def main():
    st.title("Shree5 GPT: By Tech Ninja Group")
    
    text = st.text_input('Enter your citizenship-related question:')
    
    if text:
        # Load the DistilBERT model and tokenizer
        model_name = "distilbert-base-uncased"
        tokenizer = DistilBertTokenizer.from_pretrained(model_name)
        model = DistilBertForQuestionAnswering.from_pretrained(model_name)

        # Read the context from the text file
        context = read_file("inputed.txt")

        # Split the text into smaller segments if needed
        segments = [context[i:i+512] for i in range(0, len(context), 512)]
        answers = []
        for i, segment in enumerate(segments):
            st.write(f"Generating answer for segment {i+1}...")
            answer = find_answer(text, segment, model, tokenizer)
            answers.append(answer)
        
        st.write("Answer:")
        for i, answer in enumerate(answers):
            st.write(f"Segment {i+1}: {answer}")

if __name__ == "__main__":
    main()