Working Questgen implemented

.gitignore

@@ -1,4 +1,6 @@
 venv
 .vscode
 s2v_reddit_2015_md.tar.gz
-__pycache__
+__pycache__
+s2v_old
+._s2v_old

app.py

@@ -1,3 +1,5 @@
+import pke
+from sense2vec import Sense2Vec
 import time
 import gradio as gr
 from transformers import AutoTokenizer
@@ -5,6 +7,19 @@ import os
 from pathlib import Path
 from FastT5 import get_onnx_runtime_sessions, OnnxT5
 
+commands = [
+    "curl -LO https://github.com/explosion/sense2vec/releases/download/v1.0.0/s2v_reddit_2015_md.tar.gz",
+    "tar -xvf  s2v_reddit_2015_md.tar.gz",
+]
+
+for command in commands:
+    return_code = os.system(command)
+    if return_code == 0:
+        print(f"Command '{command}' executed successfully")
+    else:
+        print(f"Command '{command}' failed with return code {return_code}")
+
+s2v = Sense2Vec().from_disk("s2v_old")
 
 trained_model_path = './t5_squad_v1/'
 
@@ -42,25 +57,10 @@ def get_question(sentence, answer, mdl, tknizer):
     dec = [tknizer.decode(ids, skip_special_tokens=True) for ids in outs]
 
     Question = dec[0].replace("question:", "")
-    Ouestion = Question.strip()
+    Question = Question.strip()
     return Question
 
 
-# context = "Ramsri loves to watch cricket during his free time"
-# answer = "cricket"
-context = "Donald Trump is an American media personality and businessman who served as the 45th president of the United States."
-answer = "Donald Trump"
-ques = get_question(context, answer, model, tokenizer)
-print("question: ", ques)
-
-
-context = gr.components.Textbox(
-    lines=5, placeholder="Enter paragraph/context here...")
-answer = gr.components.Textbox(
-    lines=3, placeholder="Enter answer/keyword here...")
-question = gr.components.Textbox(type="text", label="Question")
-
-
 def generate_question(context, answer):
     start_time = time.time()  # Record the start time
     result = get_question(context, answer, model, tokenizer)
@@ -70,10 +70,45 @@ def generate_question(context, answer):
     return result
 
 
+def generate_mcq(context):
+    extractor = pke.unsupervised.TopicRank()
+    extractor.load_document(input=context, language='en')
+    extractor.candidate_selection(pos={"NOUN", "PROPN", "ADJ"})
+    extractor.candidate_weighting()
+    keyphrases = extractor.get_n_best(n=10)
+
+    results = []
+
+    for keyword, _ in keyphrases:
+        original_keyword = keyword
+        keyword = original_keyword.lower().replace(" ", "_")
+        sense = s2v.get_best_sense(keyword)
+
+        if sense is not None:
+            most_similar = s2v.most_similar(sense, n=2)
+            distractors = [word.split("|")[0].lower().replace(
+                "_", " ") for word, _ in most_similar]
+
+            question = generate_question(context, original_keyword)
+
+            result = {
+                "Question": question,
+                "Keyword": original_keyword,
+                "Distractor1": distractors[0],
+                "Distractor2": distractors[1]
+            }
+
+            results.append(result)
+
+    return results
+
+
 iface = gr.Interface(
-    fn=generate_question,
-    inputs=[context, answer],
-    outputs=question
+    fn=generate_mcq,
+    inputs=gr.Textbox(label="Context", type='text'),
+    outputs=gr.JSON(value=list),
+    title="Questgen AI",
+    description="Enter a context to generate MCQs for keywords."
 )
 
 iface.launch()

requirements.txt

@@ -5,4 +5,7 @@ torch
 transformers 
 sentencepiece 
 progress
-psutil
+psutil
+sense2vec
+git+https://github.com/boudinfl/pke.git
+en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.6.0/en_core_web_sm-3.6.0-py3-none-any.whl