Submission: Nannan Aravazhi

.gitignore

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+submissions/Nannan_Aravazhi/venv/

submissions/Nannan_Aravazhi/README.md

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+Wikipedia Reading-Check 
+
+This project takes a Wikipedia topic as input and fetches the article text.
+
+It then summarizes it using a lightweight model and generates one short reading-check question based on the summary.
+
+Runs fully on CPU. No GPU required.
+
+How to Run
+1. Create and activate a virtual environment
+python3 -m venv submissions/<your_name>>/venv
+source submissions/<your_name>/venv/bin/activate
+
+2. Install dependencies
+pip install -r submissions/<your_name>>/requirements.txt
+
+3. Run the program 
+python submissions/<your_name>>/summarizer_qa.py
+
+
+Then enter a topic, e.g.:
+
+Enter a Wikipedia topic: cristiano ronaldo
+or
+lionel messi
+
+Example Output
+=== Summary ===
+Cristiano Ronaldo dos Santos Aveiro is a Portuguese international footballer...
+
+=== Reading Check ===
+1. How many goals has Cristiano Ronaldo scored in the Champions League?
+
+Models Used
+
+Summarizer: sshleifer/distilbart-cnn-12-6
+
+Question Generator: iarfmoose/t5-base-question-generator
+
+Model Info:
+First run downloads the model weights (~300MB summarizer, ~900MB QG).
+After the first run, everything is cached locally in:
+
+~/.cache/huggingface/hub/

submissions/Nannan_Aravazhi/requirements.txt

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+transformers
+wikipedia-api
+torch
+sentencepiece
+protobuf<5

submissions/Nannan_Aravazhi/summarizer_qa.py

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+import wikipediaapi
+from transformers import pipeline
+from typing import List, Dict
+
+
+#constants
+WIKI_CHAR_LIMIT = 1000 # limitng to avoid overflow
+SUM_MODEL = "sshleifer/distilbart-cnn-12-6" # heard it was decent + lightweight
+SUM_MAX_LEN = 200
+SUM_MIN_LEN = 50
+QG_MODEL  = "iarfmoose/t5-base-question-generator" # model for question generation
+QG_NUM_QUESTIONS = 1   # asking a reading check question
+USER_AGENT = "NannanAravazhi-AIatGT-AR-Fall2025/1.0 (contact: naravazhi3@gatech.edu)"
+
+
+
+# intiializing pipeline here to use huggingface locally
+summarizer_pipeline = pipeline("summarization", model=SUM_MODEL)
+qg_pipeline = pipeline("text2text-generation", model=QG_MODEL)
+
+
+
+
+# core functions
+def fetch_wikipedia_content(topic: str) -> str:
+   """
+   fetching text, limit to 1000, basic error handling
+   """
+   wiki = wikipediaapi.Wikipedia(
+        language="en",
+        user_agent=USER_AGENT  # setting user agent
+    )
+   page = wiki.page(topic)
+   if not page.exists():
+       raise ValueError(f"Can't find this topic: '{topic}'.")
+
+   text = (page.summary or "").strip()
+   if not text:
+        text = (page.text or "").strip()
+   if not text:
+        raise ValueError(f"No readable content on this topic: '{topic}'.")
+   return text[:WIKI_CHAR_LIMIT]
+
+
+def summarize_text(content: str) -> str:
+   """
+   creating short summary here
+   """
+   if not content or not content.strip():
+       raise ValueError("Empty content; cannot summarize.")
+   result = summarizer_pipeline(content, max_length=SUM_MAX_LEN, min_length=SUM_MIN_LEN, do_sample=False)
+   return result[0]["summary_text"].strip()
+
+
+def generate_questions(summary: str) -> List[str]:
+   """
+   quiz-style questions
+   """
+   if not summary or not summary.strip():
+        return []
+
+   prompt = f"Generate {QG_NUM_QUESTIONS} unique, short quiz-style questions based on this summary:\n{summary}"
+
+   out = qg_pipeline(
+       prompt,
+       max_length=64,
+       do_sample=True,           #sampling
+       top_p=0.92,
+       top_k=50,
+       temperature=0.9,
+       num_return_sequences=QG_NUM_QUESTIONS,
+       num_beams=QG_NUM_QUESTIONS,
+    )
+   qs = [it["generated_text"].strip() for it in out if it["generated_text"].strip()]
+   seen, deduped = set(), []
+   for q in qs:
+      if q not in seen:
+          seen.add(q)
+          deduped.append(q)
+          if len(deduped) >= QG_NUM_QUESTIONS:
+              break
+   return deduped
+
+
+
+
+# mani method
+def main() -> None:
+   print("=== AI@GT: Wikipedia Summarizer and QGen ===")
+   topic = input("Enter a Wikipedia topic: ").strip() or "Georgia Institute of Technology"
+   try:
+       content = fetch_wikipedia_content(topic)
+       summary = summarize_text(content)
+       questions = generate_questions(summary)
+
+
+       # hashmap to hold
+       result: Dict[str, object] = {
+           "topic": topic,
+           "summary": summary,
+           "questions": questions
+       }
+
+
+       print("\n=== Summary ===\n" + result["summary"])
+       print("\n=== Questions ===")
+       if questions:
+           for i, q in enumerate(questions, 1):
+               print(f"{i}. {q}")
+       else:
+           print("(No questions generated)")
+
+
+   except Exception as e:
+       print(f"\n[Error] {e}")
+
+
+if __name__ == "__main__":
+   main()