app.py

# app.py ────────────────────────────────────────────────────────────────
# Pin Gradio ≤ 3.31.0 in requirements.txt so <a target="_blank"> is kept
# and place architecture.png beside this file.
import os, re, faiss, zipfile, warnings, gradio as gr
from pathlib import Path
from typing import List
from sentence_transformers import SentenceTransformer
from PyPDF2 import PdfReader
from docx import Document
from docx.opc.exceptions import PackageNotFoundError
from openai import OpenAI

# ───────── 0. résumé → plain-text ──────────────────────────────────────
FILE = Path("my_resume.pdf")

def read_pdf(p: Path) -> str:
    return " ".join(pg.extract_text() or "" for pg in PdfReader(p).pages)

def read_docx(p: Path) -> str:
    return " ".join(par.text for par in Document(p).paragraphs if par.text.strip())

try:
    raw = read_docx(FILE)
except (PackageNotFoundError, KeyError, zipfile.BadZipFile):
    warnings.warn("Reading résumé as PDF")
    raw = read_pdf(FILE)

text = re.sub(r"\s+", " ", raw).strip()

# ───────── 0-bis. extra searchable metadata ───────────────────────────
LINK_MD = '<a href="https://www.linkedin.com/in/sriharideep/" target="_blank">' \
          'LinkedIn Profile</a>'
BLOG_MD = '<a href="https://sfdcbrewery.github.io/" target="_blank">' \
          'Technical Blog</a>'
ARCH_MD = (
    "ARCHITECTURE NOTE – The bot follows a Retrieval-Augmented Generation "
    "(RAG) design: PDF → 180-token chunks → MiniLM-L6 embeddings → FAISS "
    "similarity search → GPT-3.5-turbo answer constrained to context."
)

# make them retrievable by the RAG index (even though we’ll short-circuit)
text += f" LinkedIn: {LINK_MD} Blog: {BLOG_MD} {ARCH_MD}"

# ───────── 1. text → embeddings → FAISS ───────────────────────────────
def chunkify(t: str, max_tok: int = 180) -> List[str]:
    out, buf, n = [], [], 0
    for s in re.split(r"(?<=[.!?])\s+", t):
        w = len(s.split())
        if n + w > max_tok:
            out.append(" ".join(buf)); buf, n = [], 0
        buf.append(s); n += w
    if buf:
        out.append(" ".join(buf))
    return out

CHUNKS = chunkify(text)

embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
vecs = embedder.encode(CHUNKS, convert_to_numpy=True)
faiss.normalize_L2(vecs)
index = faiss.IndexFlatIP(vecs.shape[1]); index.add(vecs)

def retrieve(q: str, k: int = 4):
    qv = embedder.encode([q], convert_to_numpy=True); faiss.normalize_L2(qv)
    sims, idx = index.search(qv, k)
    return sims[0], [CHUNKS[i] for i in idx[0]]

# ───────── 2. OpenAI client ────────────────────────────────────────────
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
MODEL  = "gpt-3.5-turbo-0125"
SYSTEM = ("You are a helpful assistant. Answer ONLY with facts in the context. "
          "If missing, reply exactly: \"I don't know based on the resume.\"")

def overlap(a: str, b: str) -> bool:
    return bool(set(re.findall(r"\w+", a.lower())) &
                set(re.findall(r"\w+", b.lower())))

# ───────── 2-bis. guard words & static answers ─────────────────────────
SAFE = {"experience","project","certification","certifications","education",
        "skill","skills","summary","company","companies","role","linkedin",
        "website","blog","portfolio","architecture"}

STATIC_ANSWERS = {
    "linkedin": LINK_MD,
    "linked-in": LINK_MD,
    "blog": BLOG_MD,
    "architecture": ARCH_MD
}

# ───────── 2-ter. generator ───────────────────────────────────────────
def generate(msg: str) -> str:
    lower_msg = msg.lower().strip()

    # A. serve static responses verbatim
    for key, val in STATIC_ANSWERS.items():
        if key in lower_msg:
            return val

    # B. resume-related check
    if not (SAFE & set(re.findall(r"\w+", lower_msg))):
        return "Please ask something related to my résumé."

    # C. retrieve
    sims, ctxs = retrieve(msg)
    min_sim = 0.10 if len(msg.split()) < 3 else 0.25
    if max(sims) < min_sim:
        return "I don't know based on the resume."

    # D. GPT-3.5-turbo
    ctx = "\n".join(ctxs)
    ans = client.chat.completions.create(
        model=MODEL,
        messages=[
            {"role": "system", "content": SYSTEM},
            {"role": "user",   "content": f"Context:\n{ctx}"},
            {"role": "user",   "content": f"Question: {msg}"}
        ],
        max_tokens=256,
        temperature=0.2
    ).choices[0].message.content.strip()

    return ans if overlap(ans, ctx) else "I don't know based on the resume."

# ───────── 3. Gradio UI ────────────────────────────────────────────────
quick = [
    "Professional Summary","Education details","Experience",
    "Certifications","Skills","LinkedIn","Blog","Architecture"
]

def set_quick_q(q):
    return q

with gr.Blocks(theme="soft") as demo:
    with gr.Row(equal_height=True):
        with gr.Column(min_width=170, scale=0):
            gr.Markdown("### Quick questions")
            btns = [gr.Button(q) for q in quick]

        with gr.Column(scale=4):
            chat  = gr.Chatbot(type="messages", label="Résumé Bot", height=520)
            inp   = gr.Textbox(placeholder="Ask about my résumé…", show_label=False)
            state = gr.State([])
            quick_q = gr.Textbox(visible=False)   # Hidden textbox for quick button input

    # ENTER
    def user_submit(msg, hist):
        ans  = generate(msg)
        hist = hist + [{"role":"user","content":msg},
                       {"role":"assistant","content":ans}]
        return "", hist, hist

    inp.submit(user_submit, [inp, state], [inp, chat, state])

    def quick_send(hist, q):
        ans = generate(q)
        hist = hist + [{"role":"user","content":q},
                       {"role":"assistant","content":ans}]
        return hist, hist

    for b, q in zip(btns, quick):
        b.click(
            fn=lambda v=None, val=q: val,   # sets quick_q value to q
            inputs=None,
            outputs=quick_q
        ).then(
            quick_send, [state, quick_q], [chat, state]
        )

if __name__ == "__main__":
    demo.launch(share=True)