diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/.gitignore b/Domains/AI-ML/MiniProjects/academic_chatbot/.gitignore
new file mode 100644
index 00000000..c66ba3a1
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/.gitignore
@@ -0,0 +1,2 @@
+.env
+.venv
\ No newline at end of file
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/README.md b/Domains/AI-ML/MiniProjects/academic_chatbot/README.md
new file mode 100755
index 00000000..99cd372d
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/README.md
@@ -0,0 +1,36 @@
+
+**Contributor:** Mmadan128
+
+# Academic Chatbot (LangChain + Gemini)
+
+## Quickstart
+
+1. Create and activate a virtual environment.
+2. Install deps:
+```
+pip install -r requirements.txt
+```
+3. Set environment:
+```
+copy .env.example .env  # Windows
+# Set GOOGLE_API_KEY in .env
+```
+4. Ingest sample data (place PDFs/text in `data/`):
+```
+python -m app.ingest_cli example.pdf
+```
+5. Run API:
+```
+uvicorn app.server:app --reload --host 0.0.0.0 --port 8000
+```
+6. Open `public/widget.html` and set `window.CAMPUS_ASSISTANT_API` if needed.
+
+## Environment
+- GOOGLE_API_KEY: Google Generative AI key
+- EMBEDDING_MODEL: sentence-transformers multilingual model
+
+## Ingestion
+Use `/ingest` or the CLI to add PDFs/TXT. The FAISS store is persisted in `vectorstore/`.
+
+## Privacy
+No chat content is stored beyond daily JSONL logs; configure retention as needed.
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/app/__init__.py b/Domains/AI-ML/MiniProjects/academic_chatbot/app/__init__.py
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@@ -0,0 +1 @@
+__all__ = []
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diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/app/config.py b/Domains/AI-ML/MiniProjects/academic_chatbot/app/config.py
new file mode 100755
index 00000000..0e43efb2
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/app/config.py
@@ -0,0 +1,31 @@
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+BASE_DIR = Path(__file__).resolve().parent.parent
+DATA_DIR = BASE_DIR / "data"
+VECTOR_DIR = BASE_DIR / "vectorstore"
+LOGS_DIR = BASE_DIR / "logs"
+PUBLIC_DIR = BASE_DIR / "public"
+
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY", "")
+CONTACT_FALLBACK = os.getenv("CONTACT_FALLBACK", "contact@college.example")
+
+DEFAULT_EMBEDDING_MODEL = os.getenv(
+    "EMBEDDING_MODEL",
+    "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+)
+
+GEN_AI_MODEL = os.getenv("GEN_AI_MODEL", "gemini-2.0-flash")
+SIMILARITY_FALLBACK_THRESHOLD = float(os.getenv("SIMILARITY_FALLBACK_THRESHOLD", "0.35"))
+TOP_K = int(os.getenv("TOP_K", "5"))
+
+# OCR settings
+OCR_LANGS = os.getenv("OCR_LANGS", "eng+hin+ben+mar+tam").split("+")
+TESSERACT_CMD = os.getenv("TESSERACT_CMD", "")  # Optional: full path to tesseract.exe
+POPPLER_PATH = os.getenv("POPPLER_PATH", "")   # Optional: path to poppler bin (for pdf2image on Windows)
+
+for d in (DATA_DIR, VECTOR_DIR, LOGS_DIR, PUBLIC_DIR):
+    d.mkdir(parents=True, exist_ok=True)
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/app/ingest.py b/Domains/AI-ML/MiniProjects/academic_chatbot/app/ingest.py
new file mode 100755
index 00000000..f84b68a7
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/app/ingest.py
@@ -0,0 +1,234 @@
+from pathlib import Path
+from typing import Iterable, List, Optional, Tuple
+
+from langchain.docstore.document import Document
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader, TextLoader
+import pandas as pd
+import re
+
+# OCR imports
+try:
+    from pdf2image import convert_from_path
+    import pytesseract
+except Exception:
+    convert_from_path = None  # type: ignore
+    pytesseract = None  # type: ignore
+
+from .config import DATA_DIR, OCR_LANGS, TESSERACT_CMD, POPPLER_PATH
+from .vectordb import add_documents
+
+
+CHUNK_SIZE = 800
+CHUNK_OVERLAP = 120
+
+
+def _split_documents(documents: List[Document]) -> List[Document]:
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=CHUNK_SIZE,
+        chunk_overlap=CHUNK_OVERLAP,
+        separators=["\n\n", "\n", " ", ""],
+    )
+    return splitter.split_documents(documents)
+
+
+def _collect_paths(inputs: List[Path]) -> List[Path]:
+    collected: List[Path] = []
+    allowed_ext = {".pdf", ".txt", ".md", ".csv", ".xlsx", ".xls"}
+    for p in inputs:
+        if p.is_dir():
+            for fp in p.rglob("*"):
+                if fp.is_file() and fp.suffix.lower() in allowed_ext:
+                    collected.append(fp)
+        elif p.is_file() and p.suffix.lower() in allowed_ext:
+            collected.append(p)
+    return collected
+
+
+def _normalize_col(name: str) -> str:
+    name = str(name or "").strip().lower()
+    name = re.sub(r"[_\s]+", " ", name)
+    return name
+
+
+def _row_to_text(df: pd.DataFrame, row: pd.Series, extra_keywords: str = "") -> str:
+    parts: List[str] = []
+    for col in df.columns:
+        val = row.get(col, "")
+        if pd.isna(val) or str(val).strip() == "":
+            continue
+        text_val = str(val).strip()
+        parts.append(f"{col}: {text_val}")
+    if extra_keywords:
+        parts.append(f"keywords: {extra_keywords}")
+    return "\n".join(parts)
+
+
+def _detect_header_index(df_no_header: pd.DataFrame) -> int:
+    # Heuristic: first row with >=50% non-empty cells is the header
+    threshold = max(1, int(0.5 * df_no_header.shape[1]))
+    for idx in range(len(df_no_header)):
+        non_empty = (df_no_header.iloc[idx].astype(str).str.strip() != "").sum()
+        if non_empty >= threshold:
+            return idx
+    return 0
+
+
+def _read_excel_with_header(path: Path, sheet_name: str) -> pd.DataFrame:
+    # Read without header, detect header row, then reassign columns
+    df0 = pd.read_excel(path, sheet_name=sheet_name, header=None, dtype=str, keep_default_na=False)
+    if df0.empty:
+        return df0
+    header_idx = _detect_header_index(df0)
+    header = df0.iloc[header_idx].astype(str).tolist()
+    df = df0.iloc[header_idx + 1 :].copy()
+    df.columns = [_normalize_col(h) for h in header]
+    # Drop fully empty rows
+    df = df.replace({None: ""})
+    df = df[~df.isna().all(axis=1)]
+    return df
+
+
+def _sheet_keywords(sheet: str, df: pd.DataFrame) -> str:
+    kw = [sheet]
+    # If there is a department-like column, include its unique values
+    for cand in ["dept", "department", "branch", "programme", "program"]:
+        if cand in df.columns:
+            vals = sorted({str(v).strip() for v in df[cand].tolist() if str(v).strip()})
+            kw.extend(vals)
+    # Add common synonyms to aid retrieval
+    kw.extend(["faculty", "staff", "professor", "teacher", "hod", "contact"])
+    return ", ".join(sorted({k.lower() for k in kw if k}))
+
+
+def _load_tabular(path: Path) -> List[Document]:
+    docs: List[Document] = []
+    if path.suffix.lower() == ".csv":
+        df = pd.read_csv(path, dtype=str, keep_default_na=False, na_filter=False)
+        if not df.empty:
+            # Normalize columns
+            df.columns = [_normalize_col(c) for c in df.columns]
+            df = df.dropna(how="all")
+            sheet_kw = _sheet_keywords(path.stem, df)
+            for idx, row in df.iterrows():
+                text = _row_to_text(df, row, extra_keywords=sheet_kw)
+                if not text.strip():
+                    continue
+                docs.append(
+                    Document(
+                        page_content=text,
+                        metadata={"source": str(path), "type": "csv", "row": int(idx)},
+                    )
+                )
+            # Add a sheet-level summary to help queries like "CSE faculty"
+            preview_cols = df.columns[:6]
+            lines = [
+                f"Row {i}: "
+                + ", ".join(f"{c}={str(df.iloc[i][c]).strip()}" for c in preview_cols if str(df.iloc[i][c]).strip())
+                for i in range(min(25, len(df)))
+            ]
+            if lines:
+                docs.append(
+                    Document(
+                        page_content=f"Summary of {path.name}:\n" + "\n".join(lines) + f"\nkeywords: {sheet_kw}",
+                        metadata={"source": str(path), "type": "csv_summary"},
+                    )
+                )
+    else:
+        xls = pd.ExcelFile(path)
+        for sheet in xls.sheet_names:
+            df = _read_excel_with_header(path, sheet)
+            if df.empty:
+                continue
+            sheet_kw = _sheet_keywords(sheet, df)
+            for idx, row in df.iterrows():
+                text = _row_to_text(df, row, extra_keywords=sheet_kw)
+                if not text.strip():
+                    continue
+                docs.append(
+                    Document(
+                        page_content=text,
+                        metadata={
+                            "source": str(path),
+                            "type": "excel",
+                            "sheet": sheet,
+                            "row": int(idx),
+                        },
+                    )
+                )
+            # Sheet-level summary
+            preview_cols = df.columns[:6]
+            lines = [
+                f"Row {i}: "
+                + ", ".join(f"{c}={str(df.iloc[i][c]).strip()}" for c in preview_cols if str(df.iloc[i][c]).strip())
+                for i in range(min(25, len(df)))
+            ]
+            if lines:
+                docs.append(
+                    Document(
+                        page_content=f"Summary of sheet {sheet} in {path.name}:\n"
+                        + "\n".join(lines)
+                        + f"\nkeywords: {sheet_kw}",
+                        metadata={
+                            "source": str(path),
+                            "type": "excel_summary",
+                            "sheet": sheet,
+                        },
+                    )
+                )
+    return docs
+
+
+def _pdf_ocr_fallback(path: Path) -> List[Document]:
+    if convert_from_path is None or pytesseract is None:
+        return []
+    if TESSERACT_CMD:
+        pytesseract.pytesseract.tesseract_cmd = TESSERACT_CMD
+    images = convert_from_path(str(path), dpi=300, poppler_path=POPPLER_PATH or None)
+    docs: List[Document] = []
+    langs = "+".join(OCR_LANGS) if OCR_LANGS else "eng"
+    for i, img in enumerate(images):
+        text = pytesseract.image_to_string(img, lang=langs)
+        if text and text.strip():
+            docs.append(
+                Document(
+                    page_content=text,
+                    metadata={"source": str(path), "type": "pdf_ocr", "page": i + 1},
+                )
+            )
+    return docs
+
+
+def load_files(paths: List[Path]) -> List[Document]:
+    docs: List[Document] = []
+    for p in paths:
+        suf = p.suffix.lower()
+        if suf == ".pdf":
+            try:
+                loader = PyPDFLoader(str(p))
+                loaded = loader.load()
+                if not loaded or not any((d.page_content or "").strip() for d in loaded):
+                    ocr_docs = _pdf_ocr_fallback(p)
+                    docs.extend(ocr_docs)
+                else:
+                    docs.extend(loaded)
+            except Exception:
+                ocr_docs = _pdf_ocr_fallback(p)
+                docs.extend(ocr_docs)
+        elif suf in {".txt", ".md"}:
+            loader = TextLoader(str(p), encoding="utf-8")
+            docs.extend(loader.load())
+        elif suf in {".csv", ".xlsx", ".xls"}:
+            docs.extend(_load_tabular(p))
+    return docs
+
+
+def ingest_local(paths: List[str]) -> int:
+    if not paths:
+        base_inputs = [DATA_DIR]
+    else:
+        base_inputs = [Path(p) if Path(p).is_absolute() else Path(DATA_DIR) / p for p in paths]
+    file_paths = _collect_paths(base_inputs)
+    raw_docs = load_files(file_paths)
+    chunks = _split_documents(raw_docs)
+    return add_documents(chunks)
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/app/ingest_cli.py b/Domains/AI-ML/MiniProjects/academic_chatbot/app/ingest_cli.py
new file mode 100755
index 00000000..03e8b53c
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/app/ingest_cli.py
@@ -0,0 +1,15 @@
+import sys
+from pathlib import Path
+from .ingest import ingest_local
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: python -m app.ingest_cli <file1> [file2 ...]")
+        sys.exit(1)
+    added = ingest_local(sys.argv[1:])
+    print(f"Added {added} chunks")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/app/llm.py b/Domains/AI-ML/MiniProjects/academic_chatbot/app/llm.py
new file mode 100755
index 00000000..780cf42d
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/app/llm.py
@@ -0,0 +1,27 @@
+from typing import List, Tuple
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.output_parsers import StrOutputParser
+
+from .config import GOOGLE_API_KEY, GEN_AI_MODEL
+
+
+def build_llm():
+    if not GOOGLE_API_KEY:
+        raise RuntimeError("GOOGLE_API_KEY not set")
+    return ChatGoogleGenerativeAI(model=GEN_AI_MODEL, google_api_key=GOOGLE_API_KEY, temperature=0.2)
+
+
+SYSTEM_PROMPT = (
+    "You are a helpful, concise campus assistant. Answer in the user's language. "
+    "Cite from provided context only. If unsure, say you are not certain and suggest the official contact."
+)
+
+
+def build_rag_chain(retriever):
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", SYSTEM_PROMPT + "\nContext:\n{context}"),
+        ("human", "{question}"),
+    ])
+    llm = build_llm()
+    return prompt | llm | StrOutputParser()
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/app/logging_utils.py b/Domains/AI-ML/MiniProjects/academic_chatbot/app/logging_utils.py
new file mode 100755
index 00000000..6eb86606
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/app/logging_utils.py
@@ -0,0 +1,16 @@
+from __future__ import annotations
+import orjson
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict
+
+from .config import LOGS_DIR
+
+
+def log_event(event: Dict[str, Any]) -> None:
+    ts = datetime.utcnow()
+    fn = LOGS_DIR / f"conversations_{ts.strftime('%Y-%m-%d')}.jsonl"
+    record = {"ts": ts.isoformat() + "Z", **event}
+    with open(fn, "ab") as f:
+        f.write(orjson.dumps(record))
+        f.write(b"\n")
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/app/schemas.py b/Domains/AI-ML/MiniProjects/academic_chatbot/app/schemas.py
new file mode 100755
index 00000000..345b7354
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/app/schemas.py
@@ -0,0 +1,18 @@
+from pydantic import BaseModel, Field
+from typing import List, Optional
+
+
+class IngestRequest(BaseModel):
+    files: List[str] = Field(default_factory=list, description="List of filenames under data/")
+
+
+class ChatRequest(BaseModel):
+    session_id: str
+    message: str
+    language: str = "auto"
+
+
+class ChatResponse(BaseModel):
+    answer: str
+    fallback_used: bool = False
+    sources: List[str] = Field(default_factory=list)
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/app/server.py b/Domains/AI-ML/MiniProjects/academic_chatbot/app/server.py
new file mode 100755
index 00000000..896561c9
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/app/server.py
@@ -0,0 +1,78 @@
+from typing import Dict, List
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+from langchain.docstore.document import Document
+
+from .schemas import IngestRequest, ChatRequest, ChatResponse
+from .ingest import ingest_local
+from .vectordb import similarity_search
+from .llm import build_rag_chain
+from .config import TOP_K, SIMILARITY_FALLBACK_THRESHOLD, CONTACT_FALLBACK
+from .logging_utils import log_event
+
+app = FastAPI(title="Campus Assistant", version="0.1.0")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+_sessions: Dict[str, List[Dict[str, str]]] = {}
+
+
+@app.get("/health")
+async def health():
+    return {"ok": True}
+
+
+@app.post("/ingest")
+async def ingest(req: IngestRequest):
+    added = ingest_local(req.files)
+    return {"added_chunks": added}
+
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(req: ChatRequest):
+    history = _sessions.setdefault(req.session_id, [])
+
+    docs = similarity_search(req.message, k=TOP_K)
+    context_text = "\n\n".join(d.page_content for d in docs)
+    sources = list({str(d.metadata.get("source", "unknown")) for d in docs})
+
+    chain = build_rag_chain(retriever=None)
+    answer = await chain.ainvoke({"context": context_text, "question": req.message})
+
+    max_score = 0.0
+    if hasattr(docs, "scores"):
+        max_score = max((s or 0.0) for s in getattr(docs, "scores"))
+    # Heuristic: if no context or low similarity, append fallback
+    fallback_used = False
+    if not docs or len(context_text.strip()) == 0:
+        fallback_used = True
+    if fallback_used or max_score < SIMILARITY_FALLBACK_THRESHOLD:
+        fallback_used = True
+        answer = (
+            answer.strip()
+            + f"\n\nIf this doesn't answer your question, please contact: {CONTACT_FALLBACK}"
+        )
+
+    history.append({"user": req.message, "assistant": answer})
+    if len(history) > 20:
+        _sessions[req.session_id] = history[-20:]
+
+    log_event(
+        {
+            "type": "chat",
+            "session_id": req.session_id,
+            "message": req.message,
+            "answer": answer,
+            "sources": sources,
+            "fallback": fallback_used,
+        }
+    )
+
+    return ChatResponse(answer=answer, fallback_used=fallback_used, sources=sources)
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/app/vectordb.py b/Domains/AI-ML/MiniProjects/academic_chatbot/app/vectordb.py
new file mode 100755
index 00000000..43cfa51b
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/app/vectordb.py
@@ -0,0 +1,44 @@
+from pathlib import Path
+from typing import List, Optional
+
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.docstore.document import Document
+
+from .config import VECTOR_DIR, DEFAULT_EMBEDDING_MODEL
+
+
+_embedding = None
+
+
+def get_embeddings():
+    global _embedding
+    if _embedding is None:
+        _embedding = HuggingFaceEmbeddings(model_name=DEFAULT_EMBEDDING_MODEL)
+    return _embedding
+
+
+def get_vectorstore(name: str = "campus_faqs") -> FAISS:
+    path = Path(VECTOR_DIR) / f"{name}.faiss"
+    embeddings = get_embeddings()
+    if path.exists():
+        return FAISS.load_local(str(path), embeddings, allow_dangerous_deserialization=True)
+    vs = FAISS.from_texts([""], embedding=embeddings)
+    vs.save_local(str(path))
+    return vs
+
+
+def add_documents(documents: List[Document], name: str = "campus_faqs") -> int:
+    vs = get_vectorstore(name)
+    embeddings = get_embeddings()
+    if documents:
+        new_vs = FAISS.from_documents(documents, embeddings)
+        vs.merge_from(new_vs)
+        vs.save_local(str(Path(VECTOR_DIR) / f"{name}.faiss"))
+        return len(documents)
+    return 0
+
+
+def similarity_search(query: str, k: int = 5, name: str = "campus_faqs") -> List[Document]:
+    vs = get_vectorstore(name)
+    return vs.similarity_search(query, k=k)
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/CSE_Faculty.xlsx b/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/CSE_Faculty.xlsx
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diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/Facillities.xlsx b/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/Facillities.xlsx
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diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/MAIT_Administrative_Procedures.xlsx b/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/MAIT_Administrative_Procedures.xlsx
new file mode 100755
index 00000000..3771c0e6
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diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/MAIT_Societies.xlsx b/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/MAIT_Societies.xlsx
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index 00000000..35ad0dfc
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diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/academic_calendar.xlsx b/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/academic_calendar.xlsx
new file mode 100755
index 00000000..e07efeba
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diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/syllabus.csv b/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/syllabus.csv
new file mode 100755
index 00000000..d97007a9
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/syllabus.csv
@@ -0,0 +1,46 @@
+Category,Group,Code,Paper,L,P,Credits,Notes
+First Semester,,,,,,,
+Theory Papers,,,,,,,
+,ES,ES-101,Programming in 'C',3,-,3,"*Any one of the following"
+,BS,"BS-103/BS-121#",Applied Chemistry / Basic Chemistry#,3,-,3,"*Any one of the following"
+,BS,BS-105,Applied Physics – I,3,-,3,
+,ES,ES-107,Electrical Science,3,-,3,"*Any one of the following"
+,BS,BS-109,Environmental Studies,3,-,3,"*Any one of the following"
+,BS,BS-111,Applied Mathematics – I,4,-,4,
+,HS,HS-113,"Group 1: Communications Skills",3,-,3,"**Group 1 or Group 2 shall be offered"
+,HS,HS-113,"OR Group 2: Indian Constitution***",2,-,2,
+,HS,HS-117,Human Values and Ethics***,1,-,1,
+,ES,ES-119,Manufacturing Process,4,-,4,
+Practical/Viva Voce,,,,,,,
+,BS,BS-151,Physics-I Lab,-,2,1,
+,ES,ES-153,Programming in ‘C’ Lab,-,2,1,"Any of the following corresponding to the theory paper offered"
+,BS,BS-155,Applied Chemistry Lab,-,2,1,"Any of the following corresponding to the theory paper offered"
+,ES,ES-157,Engineering Graphics-I,-,4,2,"Any of the following corresponding to the theory paper offered"
+,ES,ES-159,Electrical Science Lab,-,2,1,"Any of the following corresponding to the theory paper offered"
+,BS,BS-161,Environmental Studies Lab,-,2,1,"Any of the following corresponding to the theory paper offered"
+,,,,,,,
+Total,,,,20,10,25,
+
+Category,Group,Paper Code,Paper,L,P,Credits,Notes
+Second Semester,,,,,,,
+Theory Papers,,,,,,,
+,ES,ES-102,"Programming in 'C'",3,-,3,"*Any one of the following"
+,BS,"BS-104/BS-120#","Applied Chemistry / Basic Chemistry#",3,-,3,"*Any one of the following"
+,BS,BS-106,Applied Physics – II,3,-,3,
+,ES,ES-108,Electrical Science,3,-,3,"*Any one of the following"
+,BS,BS-110,Environmental Studies,3,-,3,"*Any one of the following"
+,BS,BS-112,Applied Mathematics – II,4,-,4,
+,HS,HS-114,"Group 1: Communications Skills",3,-,3,"**Group 1 or Group 2 shall be offered"
+,HS,HS-114,"OR Group 2: Indian Constitution***",2,-,2,"**Group 1 or Group 2 shall be offered"
+,HS,HS-118,Human Values and Ethics***,1,-,1,
+,ES,ES-114,Engineering Mechanics,3,-,3,
+Practical/Viva Voce,,,,,,,
+,BS,BS-152,Physics-II Lab,-,2,1,
+,ES,ES-154,"Programming in 'C' Lab",-,2,1,"*Any of the following corresponding to the theory paper offered"
+,BS,BS-156,Applied Chemistry,-,2,1,"*Any of the following corresponding to the theory paper offered"
+,ES,ES-158,Engineering Graphics-II,-,2,1,
+,ES,ES-160,Electrical Science Lab,-,2,1,"*Any of the following corresponding to the theory paper offered"
+,BS,BS-162,Environmental Studies Lab,-,2,1,"*Any of the following corresponding to the theory paper offered"
+,ES,ES-164,Workshop Practice,-,4,2,
+,,,,,,,
+Total,,,,19,12,25,
\ No newline at end of file
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/syllabus.txt b/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/syllabus.txt
new file mode 100755
index 00000000..e69de29b
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/syllabus.xlsx b/Domains/AI-ML/MiniProjects/academic_chatbot/data/excels/syllabus.xlsx
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index 00000000..b987019a
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diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/data/pdfs/sem1_syllabus.pdf b/Domains/AI-ML/MiniProjects/academic_chatbot/data/pdfs/sem1_syllabus.pdf
new file mode 100755
index 00000000..3a50e813
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diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/main.py b/Domains/AI-ML/MiniProjects/academic_chatbot/main.py
new file mode 100755
index 00000000..d2fd166a
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/main.py
@@ -0,0 +1,4 @@
+import uvicorn
+
+if __name__ == "__main__":
+    uvicorn.run("app.server:app", host="0.0.0.0", port=8000)
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/public/widget.html b/Domains/AI-ML/MiniProjects/academic_chatbot/public/widget.html
new file mode 100755
index 00000000..5a532034
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/public/widget.html
@@ -0,0 +1,45 @@
+<!doctype html>
+<html>
+  <head>
+    <meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width, initial-scale=1"/>
+    <title>Campus Assistant Widget</title>
+    <style>
+      #chatbox{position:fixed;bottom:20px;right:20px;width:320px;background:#fff;border:1px solid #ddd;border-radius:8px;box-shadow:0 4px 12px rgba(0,0,0,.1);font-family:system-ui,-apple-system,Segoe UI,Roboto,Inter,Arial}
+      #header{padding:8px 12px;background:#0a66c2;color:#fff;border-radius:8px 8px 0 0;font-weight:600}
+      #messages{height:280px;overflow:auto;padding:10px;font-size:14px}
+      #input{display:flex;border-top:1px solid #eee}
+      #input input{flex:1;padding:10px;border:0;outline:none}
+      #input button{padding:10px 12px;border:0;background:#0a66c2;color:#fff;cursor:pointer}
+      .msg{margin:6px 0}
+      .user{font-weight:600}
+    </style>
+  </head>
+  <body>
+    <div id="chatbox">
+      <div id="header">Campus Assistant</div>
+      <div id="messages"></div>
+      <div id="input">
+        <input id="text" placeholder="Type your question..." />
+        <button id="send">Send</button>
+      </div>
+    </div>
+    <script>
+      const apiBase = (window.CAMPUS_ASSISTANT_API||'http://localhost:8000');
+      const sessionId = Math.random().toString(36).slice(2);
+      const messages = document.getElementById('messages');
+      function add(role, text){
+        const div=document.createElement('div');
+        div.className='msg';
+        div.innerHTML = '<span class="'+role+'">'+role+':</span> '+text;
+        messages.appendChild(div);messages.scrollTop=messages.scrollHeight;
+      }
+      document.getElementById('send').onclick = async ()=>{
+        const input=document.getElementById('text');
+        const q=input.value.trim(); if(!q) return; input.value=''; add('user', q);
+        const res = await fetch(apiBase+'/chat',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({session_id:sessionId,message:q})});
+        const data = await res.json(); add('assistant', data.answer);
+      };
+    </script>
+  </body>
+</html>
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/requirements.txt b/Domains/AI-ML/MiniProjects/academic_chatbot/requirements.txt
new file mode 100755
index 00000000..099d44d2
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/requirements.txt
@@ -0,0 +1,23 @@
+fastapi==0.115.0
+uvicorn[standard]==0.30.6
+pydantic==2.9.2
+python-dotenv==1.0.1
+langchain==0.3.3
+langchain-community==0.3.2
+langchain-google-genai==2.0.1
+google-generativeai==0.8.3
+sentence-transformers==3.1.1
+faiss-cpu==1.8.0.post1
+pypdf==5.0.1
+python-multipart==0.0.12
+tqdm==4.66.5
+orjson==3.10.7
+pandas==2.2.3
+openpyxl==3.1.5
+streamlit==1.40.1
+streamlit-mic-recorder==0.0.8
+SpeechRecognition==3.11.0
+gTTS==2.5.4
+pydub==0.25.1
+pdf2image==1.17.0
+pytesseract==0.3.10
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/stream_app.py b/Domains/AI-ML/MiniProjects/academic_chatbot/stream_app.py
new file mode 100755
index 00000000..2726ed85
--- /dev/null
+++ b/Domains/AI-ML/MiniProjects/academic_chatbot/stream_app.py
@@ -0,0 +1,157 @@
+import os
+import io
+import base64
+import streamlit as st
+from typing import List
+
+from app.ingest import ingest_local
+from app.vectordb import similarity_search
+from app.llm import build_rag_chain
+from app.config import TOP_K, CONTACT_FALLBACK, GOOGLE_API_KEY
+
+# Voice deps
+try:
+    from streamlit_mic_recorder import mic_recorder
+    import speech_recognition as sr
+    from gtts import gTTS
+    from pydub import AudioSegment
+except Exception:
+    mic_recorder = None
+
+st.set_page_config(page_title="Campus Assistant - Streamlit (Local)", page_icon="🎓", layout="centered")
+
+st.title("🎓 Campus Assistant - Local Prototype")
+
+if not GOOGLE_API_KEY:
+    st.error("GOOGLE_API_KEY not set. Add it to your environment or .env and restart.")
+
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+if "session_id" not in st.session_state:
+    st.session_state["session_id"] = os.urandom(6).hex()
+
+# Language selection (UI language and TTS language)
+LANG_OPTIONS = {
+    "English (en)": "en",
+    "Hindi (hi)": "hi",
+    "Bengali (bn)": "bn",
+    "Marathi (mr)": "mr",
+    "Tamil (ta)": "ta",
+    "Telugu (te)": "te",
+    "Kannada (kn)": "kn",
+    "Gujarati (gu)": "gu",
+}
+col_lang, col_tts = st.columns(2)
+with col_lang:
+    ui_lang = st.selectbox("Preferred language", list(LANG_OPTIONS.keys()), index=0)
+with col_tts:
+    tts_lang = st.selectbox("Voice reply language", list(LANG_OPTIONS.keys()), index=0)
+ui_lang_code = LANG_OPTIONS[ui_lang]
+tts_lang_code = LANG_OPTIONS[tts_lang]
+
+with st.sidebar:
+    st.header("Ingestion (Local)")
+    st.caption("Reads from the data/ directory recursively: pdfs, txt, md, csv, xlsx, xls")
+    if st.button("Ingest: All under data/"):
+        try:
+            added = ingest_local([])
+            st.success(f"Added {added} chunks")
+        except Exception as e:
+            st.error(str(e))
+    subfolders = st.text_input("Or subfolders (comma-separated)", value="pdfs, excels")
+    if st.button("Ingest: Selected subfolders"):
+        items: List[str] = [s.strip() for s in subfolders.split(",") if s.strip()]
+        try:
+            added = ingest_local(items)
+            st.success(f"Added {added} chunks from: {items}")
+        except Exception as e:
+            st.error(str(e))
+
+st.write("")
+st.caption(f"Session: {st.session_state['session_id']}")
+
+# Chat history
+for msg in st.session_state["messages"]:
+    if msg["role"] in ("user", "assistant"):
+        st.chat_message(msg["role"]).write(msg["content"]) 
+        if msg["role"] == "assistant" and msg.get("audio_b64"):
+            st.audio(msg["audio_b64"], format="audio/mp3")
+
+
+def _transcode_to_wav_bytes(raw_bytes: bytes) -> bytes:
+    # Try to decode with pydub using common browser formats, export WAV mono 16k
+    last_error = None
+    for fmt in (None, "webm", "ogg", "mp3", "wav"):
+        try:
+            seg = AudioSegment.from_file(io.BytesIO(raw_bytes), format=fmt) if fmt else AudioSegment.from_file(io.BytesIO(raw_bytes))
+            seg = seg.set_channels(1).set_frame_rate(16000)
+            out = io.BytesIO()
+            seg.export(out, format="wav")
+            out.seek(0)
+            return out.read()
+        except Exception as e:
+            last_error = e
+            continue
+    raise RuntimeError(f"Could not transcode audio to WAV. Install ffmpeg and retry. Details: {last_error}")
+
+# Input row: text + optional mic
+col_text, col_mic = st.columns([3,1])
+with col_text:
+    prompt = st.chat_input("Ask about fees, scholarships, timetable, etc.")
+voice_prompt = None
+with col_mic:
+    if mic_recorder is not None:
+        audio = mic_recorder(start_prompt="🎙️ Voice", stop_prompt="Stop", just_once=True, use_container_width=True)
+        if audio and audio.get("bytes"):
+            try:
+                wav_bytes = _transcode_to_wav_bytes(audio["bytes"]) if AudioSegment else audio["bytes"]
+                rec = sr.Recognizer()
+                with sr.AudioFile(io.BytesIO(wav_bytes)) as source:
+                    audio_data = rec.record(source)
+                    voice_prompt = rec.recognize_google(audio_data, language=ui_lang_code)
+            except Exception as e:
+                st.warning(f"Voice recognition error: {e}\nTip: install ffmpeg and ensure it's on PATH.")
+
+user_input = prompt or voice_prompt
+
+if user_input:
+    st.session_state["messages"].append({"role": "user", "content": user_input})
+    st.chat_message("user").write(user_input)
+
+    try:
+        docs = similarity_search(user_input, k=TOP_K)
+        context_text = "\n\n".join(d.page_content for d in docs)
+        sources = list({str(d.metadata.get("source", "unknown")) for d in docs})
+
+        chain = build_rag_chain(retriever=None)
+        answer = chain.invoke({"context": context_text, "question": user_input})
+        if not docs or not context_text.strip():
+            answer = answer.strip() + f"\n\nIf this doesn't answer your question, please contact: {CONTACT_FALLBACK}"
+
+        entry = {"role": "assistant", "content": answer}
+
+        # TTS synthesis
+        try:
+            tts = gTTS(text=answer, lang=tts_lang_code)
+            buf = io.BytesIO()
+            tts.write_to_fp(buf)
+            buf.seek(0)
+            audio_b64 = "data:audio/mp3;base64," + base64.b64encode(buf.read()).decode("ascii")
+            entry["audio_b64"] = audio_b64
+        except Exception:
+            pass
+
+        st.session_state["messages"].append(entry)
+        st.chat_message("assistant").write(answer)
+        if entry.get("audio_b64"):
+            st.audio(entry["audio_b64"], format="audio/mp3")
+
+        if sources:
+            with st.expander("Sources"):
+                for s in sources:
+                    st.write(f"- {s}")
+
+    except Exception as e:
+        err = f"Error: {e}"
+        st.session_state["messages"].append({"role": "assistant", "content": err})
+        st.chat_message("assistant").write(err)
diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/vectorstore/campus_faqs.faiss/index.faiss b/Domains/AI-ML/MiniProjects/academic_chatbot/vectorstore/campus_faqs.faiss/index.faiss
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diff --git a/Domains/AI-ML/MiniProjects/academic_chatbot/vectorstore/campus_faqs.faiss/index.pkl b/Domains/AI-ML/MiniProjects/academic_chatbot/vectorstore/campus_faqs.faiss/index.pkl
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