app.py

"""ResumeBot: a FLAN-T5 model fine-tuned on Sudeep's resume, with retrieval and hallucination guards.
Run:  pip install -r requirements.txt  &&  python app.py   (then open the local link it prints)"""
import os, re, json, inspect
import numpy as np, torch, gradio as gr
from huggingface_hub import hf_hub_download
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

MODEL_REPO = "sudeep1610/ResumeBot"      # the Hugging Face repo with the fine-tuned model
device = "cuda" if torch.cuda.is_available() else "cpu"

# ---- knowledge base (resume facts, profile, prompt, known questions) ----
cfg_path = "bot_config.json" if os.path.exists("bot_config.json") else hf_hub_download(MODEL_REPO, "bot_config.json")
cfg = json.load(open(cfg_path))
FACTS = [tuple(f) for f in cfg["facts"]]
PROFILE, PROMPT = cfg["profile"], cfg["prompt"]

# ---- models ----
tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_REPO).to(device).eval()
embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device=device)
bank, bank_fid = cfg["bank"], cfg["bank_fid"]
bank_vecs = embedder.encode(bank, normalize_embeddings=True, convert_to_numpy=True)

def retrieve(question, k=2):
    sims = bank_vecs @ embedder.encode([question], normalize_embeddings=True, convert_to_numpy=True)[0]
    best = {}
    for i in np.argsort(-sims):
        best.setdefault(bank_fid[i], float(sims[i]))
        if len(best) == k: break
    fids = list(best)
    return fids, best[fids[0]]

# ---- answer function with hallucination guards ----
PERSON_WORDS = {"sudeep", "candidate", "he", "his", "him", "you", "your", "resume", "cv", "profile"}
STOP = set("a an the is are was were of in on at to for and or with by from as it this that his he sudeep s".split())
GREETING = "Hi! 👋 I'm ResumeBot. Ask me anything about Sudeep: his education, skills, projects, certifications or achievements."
OUT_OF_SCOPE = ("I only answer questions about Sudeep's resume, so I won't guess about that. "
                "Try asking about his education, skills, projects, certifications or achievements.")

def words(text):
    return {w for w in re.findall(r"[a-z0-9]+", text.lower()) if w not in STOP}

def generate(prompt):
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=256).to(model.device)
    with torch.no_grad():
        out = model.generate(**inputs, max_new_tokens=128, num_beams=4, no_repeat_ngram_size=3, early_stopping=True)
    return tokenizer.decode(out[0], skip_special_tokens=True).strip()

def answer(question):
    q = question.strip()
    ql = re.findall(r"[a-z]+", q.lower())
    if not q or set(ql) <= {"hi", "hello", "hey", "hii", "good", "morning", "evening", "namaste"}:
        return GREETING, "Greeting"
    if set(ql) & {"thanks", "thank", "thankyou"}:
        return "You're welcome! Ask me anything else about Sudeep. 😊", "Greeting"
    fids, score = retrieve(q)
    about_him = bool(PERSON_WORDS & set(ql))
    if score < (0.35 if about_him else 0.55):                 # guard 1: not about the resume -> don't guess
        return OUT_OF_SCOPE, "Out of scope"
    context = " ".join(FACTS[f][1] for f in fids)
    reply = generate(PROMPT.format(context=context, question=q))
    grounded = len(words(reply) - words(context)) <= 1 and len(reply) > 3
    if not grounded:                                          # guard 2: answer must come from the resume text
        reply = FACTS[fids[0]][1]
    return reply, FACTS[fids[0]][0]

# ---- Gradio website ----

CSS = """
.gradio-container {max-width: 1100px !important; margin: auto;}
#hero {background: linear-gradient(135deg,#4f46e5 0%,#7c3aed 55%,#db2777 100%); border-radius: 18px; padding: 28px 32px; color: white;}
#hero h1 {margin: 0; font-size: 34px; color: white;}
#hero p {margin: 6px 0 0; opacity: .92; font-size: 16px; color: white;}
.card {border: 1px solid var(--border-color-primary); border-radius: 16px; padding: 18px 20px; background: var(--block-background-fill);}
.card h3 {margin: 0 0 8px; font-size: 15px; text-transform: uppercase; letter-spacing: .06em; opacity: .7;}
.chip {display: inline-block; padding: 4px 10px; margin: 3px 4px 3px 0; border-radius: 999px; font-size: 13px;
       background: rgba(99,102,241,.12); border: 1px solid rgba(99,102,241,.35);}
.meta {font-size: 14px; line-height: 1.8;}
footer {display: none !important;}
"""
P = PROFILE
HERO = f"""<div id="hero"><h1>🤖 ResumeBot</h1>
<p>Chat with my personal AI, a FLAN-T5 model fine-tuned on my resume. It answers only from verified resume facts.</p></div>"""
PROFILE_CARD = f"""<div class="card"><h3>Profile</h3>
<div style="font-size:24px;font-weight:700">{P['name']}</div><div style="opacity:.8;margin-bottom:10px">{P['role']}</div>
<div class="meta">📍 {P['location']}<br>🎓 {P['education']}<br>✉️ {P['email']}<br>
🔗 <a href="https://{P['linkedin']}" target="_blank">LinkedIn</a> · <a href="https://{P['github']}" target="_blank">GitHub</a></div></div>
<div class="card" style="margin-top:12px"><h3>Skills</h3>{''.join(f'<span class="chip">{s}</span>' for s in P['skills'])}</div>
<div class="card" style="margin-top:12px"><h3>How it works</h3><div class="meta">
1️⃣ Finds the matching resume section<br>2️⃣ Fine-tuned FLAN-T5 writes the answer<br>3️⃣ Answer is checked against the resume</div></div>"""

def chat(message, history):
    reply, source = answer(message)
    if source in ("Greeting",):
        return reply
    return f"{reply}\n\n<sub>📄 Resume section: {source}</sub>" if source != "Out of scope" else f"{reply}\n\n<sub>🛡️ Out of scope</sub>"

theme = gr.themes.Soft(primary_hue="indigo", secondary_hue="violet", font=[gr.themes.GoogleFont("Inter"), "sans-serif"])
style = dict(theme=theme, css=CSS)
new_gradio = "css" in inspect.signature(gr.Blocks.launch).parameters        # Gradio 6 moved theme/css to launch()

with gr.Blocks(title="ResumeBot | Sudeep", **({} if new_gradio else style)) as demo:
    gr.HTML(HERO)
    with gr.Row(equal_height=False):
        with gr.Column(scale=1, min_width=280):
            gr.HTML(PROFILE_CARD)
        with gr.Column(scale=2):
            gr.ChatInterface(
                fn=chat,
                chatbot=gr.Chatbot(height=460, show_label=False, placeholder="Ask me about Sudeep 👇"),
                textbox=gr.Textbox(placeholder="e.g. What projects has Sudeep built?", show_label=False, scale=7),
                examples=["Tell me about Sudeep", "What are his technical skills?", "What is his best project?",
                          "Which certifications does he have?", "Why should we hire him?", "Has he done any internships?"],
            )

demo.launch(**(style if new_gradio else {}))