import streamlit as st
from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
# Set page configuration
st.set_page_config(
page_title="Question Answering App",
page_icon="❓",
layout="centered",
initial_sidebar_state="auto",
)
# Page title with custom style
st.markdown(
"""
📚 Question Answering App
Enter a context and question to get precise answers powered by AI.
""",
unsafe_allow_html=True,
)
# Sidebar for model settings and context input
st.sidebar.header("Model Settings")
model_checkpoint = st.sidebar.text_input(
"Model Checkpoint", "Diezu/viedumrc", help="Specify the model checkpoint to use."
)
model_checkpoint1 = 'Diezu/viedumrc'
question_answerer = pipeline("question-answering", model=model_checkpoint1)
st.sidebar.markdown(
"""
Using model: Diezu/viedumrc.
""",
unsafe_allow_html=True,
)
context_sidebar = st.sidebar.text_area(
"Context",
"",
help="Enter the context that contains information for answering questions.",
height=200,
placeholder="Provide context for your question...",
)
# # Load the tokenizer and model with error handling
# try:
# tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
# model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)
# question_answerer = pipeline("question-answering", model=model, tokenizer=tokenizer)
# except Exception as e:
# st.error(f"Failed to load model or tokenizer: {e}", icon="🚨")
# st.stop()
# Main application
st.markdown(
"""
Provide Context and Question
""",
unsafe_allow_html=True,
)
# Input: question
question = st.text_input(
"Question",
"",
help="Write the question you want to ask about the provided context.",
placeholder="What is your question?",
)
# Button to get answer
if st.button("Get Answer"):
if context_sidebar.strip() == "" or question.strip() == "":
st.warning("Please provide both context and a question!", icon="⚠️")
else:
try:
# Using context from the sidebar and question from the main section
result = question_answerer(question=question, context=context_sidebar)
st.success("Answer Found!", icon="✅")
st.markdown(
f"""
Answer: {result['answer']}
""",
unsafe_allow_html=True,
)
except Exception as e:
st.error(f"Error while processing: {e}", icon="🚨")
# Footer with custom style
st.markdown(
"""
""",
unsafe_allow_html=True,
)
# import streamlit as st
# from transformers import pipeline
# st.title('Question Answering')
# model_checkpoint = 'Diezu/dieumrc'
# question_answerer = pipeline("question-answering", model=model_checkpoint)
# context = st.text_area('CONTEXT')
# question = st.text_input('QUESTION')
# if st.button('ANSWER'):
# i=question_answerer(question,context)
# #st.write(i['answer'])
# st.markdown(i['answer'])
# #st.write('