rib

.gitignore

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+s2v_reddit_2015_md

app.py

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+import streamlit as st
+from sense2vec import Sense2Vec
+import spacy
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load models
+tokenizer = T5Tokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
+model = T5ForConditionalGeneration.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
+
+# Load SpaCy model and Sense2Vec
+nlp = spacy.load("en_core_web_sm")
+s2v = Sense2Vec().from_disk('s2v_reddit_2015_md/s2v_old')
+
+# Function to get important keywords
+def get_imp_keywords(content):
+    out = []
+    doc = nlp(content)
+    for token in doc:
+        if token.pos_ in ["NOUN", "PROPN", "VERB", "NUM"]:
+            out.append(token.text)
+    return out
+
+# Function to generate question
+def get_question(context, answer):
+    sentence = extract_relevant_sentence(context, answer)
+    prompt = f"answer: {answer}  context: {context} </s>"
+    max_len = 256
+    encoding = tokenizer.encode_plus(prompt, max_length=max_len, pad_to_max_length=False, truncation=True, return_tensors="pt")
+
+    input_ids = encoding["input_ids"]
+    attention_mask = encoding["attention_mask"]
+
+    try:
+        outs = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            early_stopping=True,
+            num_beams=5,
+            num_return_sequences=1,
+            no_repeat_ngram_size=2,
+            max_length=300
+        )
+        dec = [tokenizer.decode(ids, skip_special_tokens=True) for ids in outs]
+        question = dec[0].replace("question:", "").strip()
+        return question
+    except Exception as e:
+        return f"Error generating question: {str(e)}"
+
+# Extract relevant sentence
+def extract_relevant_sentence(context, answer):
+    sentences = context.split(". ")
+    for sentence in sentences:
+        if answer in sentence:
+            return sentence
+    return context
+
+# Function to get similar words using Sense2Vec
+def sense2vec_get_words(word):
+    output = []
+    word = word.lower().replace(" ", "_")
+    sense = s2v.get_best_sense(word)
+    if not sense:
+        return output
+
+    most_similar = s2v.most_similar(sense, n=20)
+    for each_word in most_similar:
+        append_word = each_word[0].split("|")[0].replace("_", " ").lower()
+        if append_word.lower() != word:
+            output.append(append_word.title())
+    return output
+
+# Generate multiple questions
+def generate_question(context):
+    np = get_imp_keywords(context)
+    output = ""
+    cnt = 0
+    for answer in np:
+        if cnt >= 5:
+            break
+        distractors = sense2vec_get_words(answer.capitalize())
+        if len(distractors) > 0:
+            ques = get_question(context, answer)
+            output += f"Question: {ques}\n"
+            output += f"Answer: {answer.capitalize()}\n"
+            output += "Options:\n"
+            options = [answer.capitalize()] + distractors[:3]
+            for idx, option in enumerate(options, 1):
+                output += f"{idx}. {option}\n"
+            cnt += 1
+        output += "\n"
+    return output
+
+# Streamlit Interface
+st.title("Automatic Question Generator")
+st.write("Generate multiple-choice questions based on the provided context.")
+
+context = st.text_area("Enter the context text here:")
+
+if st.button("Generate Questions"):
+    if context:
+        with st.spinner("Generating questions..."):
+            questions_output = generate_question(context)
+            st.text_area("Generated Questions", questions_output, height=300)
+    else:
+        st.warning("Please enter the context text to generate questions.")

requirements.txt

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+sense2vec
+spacy
+transformers
+torch
+streamlit

setup.sh

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+#!/bin/bash
+python -m spacy download en_core_web_sm