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import gradio as gr
import joblib
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer
# Load the model
loaded_model = joblib.load("qa_model.joblib")
vectorizer = loaded_model["vectorizer"]
tfidf_matrix = loaded_model["tfidf_matrix"]
paragraphs = loaded_model["paragraphs"]
def answer_question(question):
question_vector = vectorizer.transform([question])
similarities = cosine_similarity(question_vector, tfidf_matrix)
most_similar_paragraph_index = np.argmax(similarities)
most_similar_paragraph = paragraphs[most_similar_paragraph_index]
paragraph_sentences = most_similar_paragraph.split(".")
best_sentence = ""
max_overlap = 0
question_words = set(question.lower().split())
for sentence in paragraph_sentences:
sentence = sentence.strip()
if not sentence:
continue
sentence_words = set(sentence.lower().split())
overlap = len(question_words.intersection(sentence_words))
if overlap > max_overlap:
max_overlap = overlap
best_sentence = sentence
return best_sentence.strip()
iface = gr.Interface(
fn=answer_question,
inputs=gr.Textbox(lines=2, placeholder="Enter your question here..."),
outputs="text",
title="Mahabharata Question Answering",
description="Ask a question about the Mahabharata, and the model will attempt to answer it.",
)
iface.launch()
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