[Feature] Multiple Choice type questions

backend/server.py

@@ -1,114 +1,191 @@
 import http.server
+import json
+import random
 import socketserver
-import urllib.parse
+import requests
 import torch
+from models.modelC.distractor_generator import DistractorGenerator
 from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import json
 
-IP='127.0.0.1'
-PORT=8000
+IP = "127.0.0.1"
+PORT = 8000
 
-def summarize(text):
-    summarizer=pipeline('summarization')
-    return summarizer(text,max_length=110)[0]['summary_text']
 
-
-def generate_question(context,answer,model_path, tokenizer_path):
+def summarize(text):
+    summarizer = pipeline("summarization")
+    return summarizer(text, max_length=110)[0]["summary_text"]
+
+def get_distractors_conceptnet(word, context):
+    word = word.lower()
+    original_word = word
+    if len(word.split()) > 0:
+        word = word.replace(" ", "_")
+    distractor_list = []
+    # context_sentences = context.split(".")
+    try:
+        relationships = ["/r/PartOf", "/r/IsA", "/r/HasA"]
+
+        for rel in relationships:
+            url = f"http://api.conceptnet.io/query?node=/c/en/{word}/n&rel={rel}&start=/c/en/{word}&limit=5"
+            if context:
+                url += f"&context={context}"
+
+            obj = requests.get(url).json()
+
+            for edge in obj["edges"]:
+                word2 = edge["end"]["label"]
+                if (
+                    word2 not in distractor_list
+                    and original_word.lower() not in word2.lower()
+                ):
+                    distractor_list.append(word2)
+        return distractor_list
+
+    except json.decoder.JSONDecodeError as e:
+        print(f"Error decoding JSON from ConceptNet API: {e}")
+        return distractor_list
+    except requests.RequestException as e:
+        print(f"Error making request to ConceptNet API: {e}")
+        return distractor_list
+
+
+def generate_question(context, answer, model_path, tokenizer_path):
     model = T5ForConditionalGeneration.from_pretrained(model_path)
     tokenizer = T5Tokenizer.from_pretrained(tokenizer_path)
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model.to(device)
 
-    input_text=f'answer: {answer} context: {context}'
+    input_text = f"answer: {answer} context: {context}"
 
-    inputs=tokenizer.encode_plus(
+    inputs = tokenizer.encode_plus(
         input_text,
-        padding='max_length',
+        padding="max_length",
         truncation=True,
         max_length=512,
-        return_tensors='pt'
+        return_tensors="pt",
     )
 
-    input_ids=inputs['input_ids'].to(device)
-    attention_mask=inputs['attention_mask'].to(device)
+    input_ids = inputs["input_ids"].to(device)
+    attention_mask = inputs["attention_mask"].to(device)
 
     with torch.no_grad():
-        output=model.generate(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            max_length=32
+        output = model.generate(
+            input_ids=input_ids, attention_mask=attention_mask, max_length=32
         )
 
     generated_question = tokenizer.decode(output[0], skip_special_tokens=True)
     return generated_question
 
-def generate_keyphrases(abstract, model_path,tokenizer_path):
-    device= torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+def generate_keyphrases(abstract, model_path, tokenizer_path):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model = T5ForConditionalGeneration.from_pretrained(model_path)
     tokenizer = T5Tokenizer.from_pretrained(tokenizer_path)
     model.to(device)
     # tokenizer.to(device)
-    input_text=f'detect keyword: abstract: {abstract}'
-    input_ids=tokenizer.encode(input_text, truncation=True,padding='max_length',max_length=512,return_tensors='pt').to(device)
-    output=model.generate(input_ids)
-    keyphrases= tokenizer.decode(output[0],skip_special_tokens=True).split(',')
-    return [x.strip() for x in keyphrases if x != '']
+    input_text = f"detect keyword: abstract: {abstract}"
+    input_ids = tokenizer.encode(
+        input_text,
+        truncation=True,
+        padding="max_length",
+        max_length=512,
+        return_tensors="pt",
+    ).to(device)
+    output = model.generate(input_ids)
+    keyphrases = tokenizer.decode(output[0], skip_special_tokens=True).split(",")
+    return [x.strip() for x in keyphrases if x != ""]
+
 
-def generate_qa(text):
+def generate_qa(self, text, question_type):
+    modelA, modelB = "./models/modelA", "./models/modelB"
+    tokenizerA, tokenizerB = "t5-base", "t5-base"
+    if question_type == "text":
+        text_summary = text
+        answers = generate_keyphrases(text_summary, modelA, tokenizerA)
+        qa = {}
+        for answer in answers:
+            question = generate_question(text_summary, answer, modelB, tokenizerB)
+            qa[question] = answer
 
-    # text_summary=summarize(text)
-    text_summary=text
-
+        return qa
 
-    modelA, modelB='./models/modelA','./models/modelB'
-    # tokenizerA, tokenizerB= './tokenizers/tokenizerA', './tokenizers/tokenizerB'
-    tokenizerA, tokenizerB= 't5-base', 't5-base'
+    elif question_type == "mcq":
+        text_summary = text
 
-    answers=generate_keyphrases(text_summary, modelA, tokenizerA)
+        answers = generate_keyphrases(text_summary, modelA, tokenizerA)
 
-    qa={}
-    for answer in answers:
-        question= generate_question(text_summary, answer, modelB, tokenizerB)
-        qa[question]=answer
-
-    return qa
-
+        qa = {}
+        for answer in answers:
+            question = generate_question(text_summary, answer, modelB, tokenizerB)
+            conceptnet_distractors = get_distractors_conceptnet(answer, text_summary)
+            t5_distractors = self.distractor_generator.generate(
+                5, answer, question, text_summary
+            )
 
+            dist_temp = list(set(conceptnet_distractors + t5_distractors))
+            dist = [x for x in dist_temp if x.lower() != answer.lower()]
+            print(conceptnet_distractors)
 
+            if len(dist) < 1:
+                distractors = []
+                qa[question] = answer
+            else:
+                distractors = random.sample(dist, min(3, len(dist)))
+                options = distractors + [answer]
+                random.shuffle(options)
 
+                formatted_question = f"{question} Options: {', '.join(options)}"
+
+                qa[formatted_question] = answer
+
+        return qa
 
 
 class QARequestHandler(http.server.BaseHTTPRequestHandler):
 
-    def do_POST(self):
+    def do_OPTIONS(self):
+        self.send_response(200)
+        self.send_header("Access-Control-Allow-Origin", "*")
+        self.send_header("Access-Control-Allow-Methods", "POST, OPTIONS")
+        self.send_header("Access-Control-Allow-Headers", "Content-Type")
+        self.send_header("Content-Length", "0")
+        self.end_headers()
 
+    def do_POST(self):
         self.send_response(200)
         self.send_header("Content-type", "text/plain")
         self.end_headers()
 
-        content_length=int(self.headers["Content-Length"])
-        post_data=self.rfile.read(content_length).decode('utf-8')
-
-        # parsed_data=urllib.parse.parse_qs(post_data)
+        content_length = int(self.headers["Content-Length"])
+        post_data = self.rfile.read(content_length).decode("utf-8")
         parsed_data = json.loads(post_data)
+        if self.path == "/":
+            input_text = parsed_data.get("input_text")
+            question_type = self.headers.get("Question-Type", "text")
 
+            qa = generate_qa(self, input_text, question_type)
 
-        input_text=parsed_data.get('input_text')
-
-        qa=generate_qa(input_text)
+            self.wfile.write(json.dumps(qa).encode("utf-8"))
+            self.wfile.flush()
 
 
+class CustomRequestHandler(QARequestHandler):
+    def __init__(self, *args, **kwargs):
+        self.distractor_generator = kwargs.pop("distractor_generator")
+        super().__init__(*args, **kwargs)
 
-        self.wfile.write(json.dumps(qa).encode("utf-8"))
-        self.wfile.flush()
 
 def main():
-    with socketserver.TCPServer((IP, PORT), QARequestHandler) as server:
-        print(f'Server started at http://{IP}:{PORT}')
+    distractor_generator = DistractorGenerator()
+    with socketserver.TCPServer(
+        (IP, PORT),
+        lambda x, y, z: CustomRequestHandler(
+            x, y, z, distractor_generator=distractor_generator
+        ),
+    ) as server:
+        print(f"Server started at http://{IP}:{PORT}")
         server.serve_forever()
 
-if __name__=="__main__":
-    main()
 
-
+if __name__ == "__main__":
+    main()

extension/html/text_input.html

@@ -2,8 +2,6 @@
 <html>
     <head>
         <title>EduAid: Text Input</title>
-        <!-- <link href="https://fonts.googleapis.com/css?family=Roboto:400,500" rel="stylesheet">
-        <link rel="stylesheet" href="./popup.css"> -->
         <script src="../pdfjs-3.9.179-dist/build/pdf.js"></script>
         <link href='https://fonts.googleapis.com/css?family=Inter' rel='stylesheet'>
         <link rel="stylesheet" href="../styles/text_input.css">
@@ -24,11 +22,10 @@ <h3>Generate QnA</h3>
                 <button id="back-button">Back</button>
                 <button id="next-button">Next</button>
             </div>
-            <!-- ******************* -->
+            <button id="mcq-button">Generate MCQ</button>
             <div id="loading-screen" class="loading-screen">
                 <div class="loading-spinner"></div>
             </div>
-            <!-- ****************** -->
         </main>
         <script src="../js/text_input.js"></script>
     </body>

extension/js/question_generation.js

@@ -1,56 +1,61 @@
-document.addEventListener("DOMContentLoaded", function(){
-    const saveButton= document.getElementById("save-button");
-    const backButton= document.getElementById("back-button");
-    const viewQuestionsButton = document.getElementById("view-questions-button");
-    const qaPairs=JSON.parse(localStorage.getItem("qaPairs"));
-    const modalClose= document.querySelector("[data-close-modal]");
-    const modal=document.querySelector("[data-modal]");
-
-
-    viewQuestionsButton.addEventListener("click", function(){
-      const modalQuestionList = document.getElementById("modal-question-list");
-      modalQuestionList.innerHTML = ""; // Clear previous content
-
-      for (const [question, answer] of Object.entries(qaPairs)) {
-          const questionElement = document.createElement("li");
-          questionElement.textContent = `Question: ${question}, Answer: ${answer}`;
-          modalQuestionList.appendChild(questionElement)
+document.addEventListener("DOMContentLoaded", function () {
+  const saveButton = document.getElementById("save-button");
+  const backButton = document.getElementById("back-button");
+  const viewQuestionsButton = document.getElementById("view-questions-button");
+  const qaPairs = JSON.parse(localStorage.getItem("qaPairs"));
+  const modalClose = document.querySelector("[data-close-modal]");
+  const modal = document.querySelector("[data-modal]");
+
+  viewQuestionsButton.addEventListener("click", function () {
+    const modalQuestionList = document.getElementById("modal-question-list");
+    modalQuestionList.innerHTML = "";
+
+    for (const [question, answer] of Object.entries(qaPairs)) {
+      const questionElement = document.createElement("li");
+      if (question.includes("Options:")) {
+        const options = question.split("Options: ")[1].split(", ");
+        const formattedOptions = options.map(
+          (opt, index) => `${String.fromCharCode(97 + index)}) ${opt}`
+        );
+        questionElement.textContent = `Question: ${
+          question.split(" Options:")[0]
+        }\n${formattedOptions.join("\n")}`;
+      } else {
+        questionElement.textContent = `Question: ${question}\n\nAnswer: ${answer}\n`;
       }
-      modal.showModal();
-    });
 
-    modalClose.addEventListener("click", function(){
-      modal.close();
-    });
-    saveButton.addEventListener("click", async function(){
-      let textContent= "EduAid Generated QnA:\n\n";
+      modalQuestionList.appendChild(questionElement);
+    }
+    modal.showModal();
+  });
 
-      for (const [question,answer] of Object.entries(qaPairs)){
-        textContent+= `Question: ${question}\nAnswer: ${answer}\n\n`;
-      }
-      const blob = new Blob([textContent], { type: "text/plain" });
-
-        // Create a URL for the Blob
-        const blobUrl = URL.createObjectURL(blob);
-
-        // Create a temporary <a> element to trigger the download
-        const downloadLink = document.createElement("a");
-        downloadLink.href = blobUrl;
-        downloadLink.download = "questions_and_answers.txt";
-        downloadLink.style.display = "none";
-
-        // Append the <a> element to the document
-        document.body.appendChild(downloadLink);
-
-        // Simulate a click on the link to trigger the download
-        downloadLink.click();
-
-        // Clean up: remove the temporary <a> element and revoke the Blob URL
-        document.body.removeChild(downloadLink);
-        URL.revokeObjectURL(blobUrl);
-    });
-
-      backButton.addEventListener("click", function(){
-        window.location.href="../html/text_input.html"
-      });
-});
+  modalClose.addEventListener("click", function () {
+    modal.close();
+  });
+  saveButton.addEventListener("click", async function () {
+    let textContent = "EduAid Generated QnA:\n\n";
+
+    for (const [question, answer] of Object.entries(qaPairs)) {
+      textContent += `Question: ${question}\nAnswer: ${answer}\n\n`;
+    }
+    const blob = new Blob([textContent], { type: "text/plain" });
+
+    const blobUrl = URL.createObjectURL(blob);
+
+    const downloadLink = document.createElement("a");
+    downloadLink.href = blobUrl;
+    downloadLink.download = "questions_and_answers.txt";
+    downloadLink.style.display = "none";
+
+    document.body.appendChild(downloadLink);
+
+    downloadLink.click();
+
+    document.body.removeChild(downloadLink);
+    URL.revokeObjectURL(blobUrl);
+  });
+
+  backButton.addEventListener("click", function () {
+    window.location.href = "../html/text_input.html";
+  });
+});