app.py

import os
import sys
import requests

# SQLite workaround (needed for Chroma on HF Spaces)
try:
    __import__("pysqlite3")
    sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")
except Exception:
    pass

import gradio as gr

from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader
from langchain_text_splitters import CharacterTextSplitter
from langchain_chroma import Chroma
from langchain_huggingface import HuggingFaceEmbeddings


# ========================
# CONFIG
# ========================
DOCS_DIR = "multiple_docs"
DB_DIR = "./db"

DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
DEEPSEEK_API_URL = "https://api.deepseek.com/v1/chat/completions"

WELCOME_MESSAGE = (
    "Hello, I'm Thierry Decae's chatbot. You can ask me recruitment-related "
    "questions about my experience, skills, availability, work eligibility, "
    "projects, and background. You can chat with me in multiple languages."
)


# ========================
# DEEPSEEK CALL
# ========================
def call_deepseek(messages):
    if not DEEPSEEK_API_KEY:
        return "Missing DEEPSEEK_API_KEY."

    headers = {
        "Authorization": f"Bearer {DEEPSEEK_API_KEY}",
        "Content-Type": "application/json",
    }

    payload = {
        "model": "deepseek-chat",
        "messages": messages,
        "temperature": 0.4,
        "max_tokens": 700,
    }

    response = requests.post(DEEPSEEK_API_URL, headers=headers, json=payload, timeout=60)
    response.raise_for_status()

    return response.json()["choices"][0]["message"]["content"].strip()


# ========================
# LOAD DOCS
# ========================
def load_documents():
    docs = []

    for f in os.listdir(DOCS_DIR):
        path = os.path.join(DOCS_DIR, f)

        try:
            if f.endswith(".pdf"):
                docs.extend(PyPDFLoader(path).load())
            elif f.endswith(".docx"):
                docs.extend(Docx2txtLoader(path).load())
            elif f.endswith(".txt"):
                docs.extend(TextLoader(path, encoding="utf-8").load())
        except Exception as e:
            print(f"Error loading {f}: {e}", flush=True)

    if not docs:
        raise ValueError("No documents found")

    splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    return splitter.split_documents(docs)


# ========================
# VECTORSTORE
# ========================
def build_vectorstore():
    print("Loading embeddings...", flush=True)

    embedding = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2"
    )

    docs = load_documents()
    print(f"Loaded {len(docs)} chunks", flush=True)

    return Chroma.from_documents(
        docs,
        embedding,
        persist_directory=DB_DIR,
    )


vectorstore = build_vectorstore()
retriever = vectorstore.as_retriever(search_kwargs={"k": 6})


# ========================
# HISTORY FORMAT
# ========================
def format_history(history):
    if not history:
        return ""

    lines = []
    for msg in history[-8:]:
        role = msg.get("role")
        content = msg.get("content")
        if role and content:
            lines.append(f"{role}: {content}")

    return "\n".join(lines)


# ========================
# MAIN QA FUNCTION
# ========================
def answer_question(query, history):
    if history is None:
        history = [{"role": "assistant", "content": WELCOME_MESSAGE}]

    if not query.strip():
        return "", history

    try:
        docs = retriever.invoke(query)
        context = "\n\n".join(d.page_content for d in docs if d.page_content)

        history_text = format_history(history)

        system_prompt = """
You are Thierry Decae's recruitment chatbot.

Answer questions about Thierry's experience, skills, and career.
Use only provided context.
If unsure, say "I'm not sure about that."
Always answer as Thierry ("I", "my").
"""

        user_prompt = f"""
Conversation:
{history_text}

Context:
{context}

Question:
{query}

Answer:
"""

        answer = call_deepseek([
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt},
        ])

    except Exception as e:
        print(e, flush=True)
        answer = "Error while answering."

    history.append({"role": "user", "content": query})
    history.append({"role": "assistant", "content": answer})

    return "", history


def clear_chat():
    return [{"role": "assistant", "content": WELCOME_MESSAGE}]


# ========================
# UI
# ========================
guest_img = os.path.join(DOCS_DIR, "Guest.jpg")
thierry_img = os.path.join(DOCS_DIR, "Thierry Picture.jpg")

avatars = None
if os.path.exists(guest_img) and os.path.exists(thierry_img):
    avatars = [guest_img, thierry_img]


with gr.Blocks() as demo:
    gr.Markdown("# Thierry Decae's Personal Assistant")

    chatbot = gr.Chatbot(
        value=[{"role": "assistant", "content": WELCOME_MESSAGE}],
        avatar_images=avatars,
        height=500,
    )

    msg = gr.Textbox(placeholder="Ask a question...")

    clear = gr.Button("Clear")

    msg.submit(answer_question, [msg, chatbot], [msg, chatbot])
    clear.click(clear_chat, None, chatbot)


demo.launch(
    server_name="0.0.0.0",
    server_port=int(os.getenv("PORT", 7860)),
)