| import streamlit as st | |
| import torch | |
| from transformers import BartForConditionalGeneration, BartTokenizer | |
| # Load the model and tokenizer | |
| model_repo_path = 'AbdurRehman313/hotpotQA_BART_Finetuned_E5' | |
| model = BartForConditionalGeneration.from_pretrained(model_repo_path) | |
| tokenizer = BartTokenizer.from_pretrained(model_repo_path) | |
| # Ensure the model is in evaluation mode | |
| model.eval() | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| model.to(device) | |
| # Streamlit app layout | |
| st.title("Multi-Hop Question Answering Application") | |
| # User input for context and question | |
| context_input = st.text_area("Enter context", height=200) | |
| question_input = st.text_area("Enter question") | |
| # Generate the answer | |
| if st.button("Get Answer"): | |
| if context_input and question_input: | |
| with st.spinner("Generating answer..."): | |
| try: | |
| # Prepare the input for the model | |
| input_text = f"context: {context_input} question: {question_input}" | |
| inputs = tokenizer(input_text, return_tensors='pt') | |
| inputs = {key: value.to(device) for key, value in inputs.items()} | |
| # Perform inference | |
| with torch.no_grad(): | |
| outputs = model.generate(inputs['input_ids'], max_length=50) | |
| # Decode the output | |
| answer = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| st.subheader("Answer") | |
| st.write(answer) | |
| except Exception as e: | |
| st.error(f"Error during question answering: {e}") | |
| else: | |
| st.warning("Please enter both context and question.") | |
| # import streamlit as st | |
| # import requests | |
| # import torch | |
| # from transformers import pipeline | |
| # from transformers import T5ForConditionalGeneration, T5Tokenizer | |
| # # Replace with your Hugging Face model repository path | |
| # model_repo_path = 'AbdurRehman313/hotpotQA_BART_Finetuned_E5' | |
| # # Load the model and tokenizer | |
| # model = T5ForConditionalGeneration.from_pretrained(model_repo_path) | |
| # tokenizer = T5Tokenizer.from_pretrained(model_repo_path) | |
| # #Take model in evaluation mode | |
| # model.eval() | |
| # # Example input question and context (replace with your actual inputs) | |
| # # question = "What is the capital of France?" | |
| # # context = "France is a country in Europe. Its capital is Paris, which is known for its art, culture, and history." | |
| # # print(f'Predicted answer: {answer}') | |
| # ### | |
| # # Prepare the input for the model | |
| # # input_text = f"context: {context} question: {question} " | |
| # # inputs = tokenizer(input_text, return_tensors='pt') | |
| # # # Move inputs to the appropriate device | |
| # # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # # model.to(device) | |
| # # inputs = {key: value.to(device) for key, value in inputs.items()} | |
| # # # Perform inference | |
| # # with torch.no_grad(): | |
| # # outputs = model.generate(inputs['input_ids'], max_length=50) | |
| # # # Decode the output | |
| # # answer = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # # Streamlit app layout | |
| # st.title("Text Summarization App") | |
| # # User input | |
| # text_input = st.text_area("Enter text to summarize", height=300) | |
| # # Summarize the text | |
| # if st.button("Summarize"): | |
| # if text_input: | |
| # with st.spinner("Generating summary..."): | |
| # try: | |
| # summary = summarizer(text_input, max_length=150, min_length=30, do_sample=False) | |
| # st.subheader("Summary") | |
| # st.write(summary[0]['summary_text']) | |
| # except Exception as e: | |
| # st.error(f"Error during summarization: {e}") | |
| # else: | |
| # st.warning("Please enter some text to summarize.") |