import os
import faiss
import numpy as np
import requests
from sentence_transformers import SentenceTransformer
import re

# 1. Charger les fichiers Markdown et enrichir le contexte
def collect_markdown_files(root_dir):
    texts, sources, raw_contents = [], [], []
    for root, dirs, files in os.walk(root_dir):
        for f in files:
            if f.endswith(".md"):
                full_path = os.path.join(root, f)
                rel_path = os.path.relpath(full_path, root_dir)
                try:
                    with open(full_path, "r", encoding="utf-8") as file:
                        content = file.read().strip()
                        if content:
                            enriched = f"[Fichier : {rel_path}]\n\n{content}"
                            texts.append(enriched)
                            sources.append(full_path)
                            raw_contents.append(content)
                except Exception as e:
                    print(f"Erreur lecture {full_path}: {e}")
    return texts, sources, raw_contents

# 2. Initialisation
ROOT_DIR = "mes_fiches"
print("🔍 Chargement des fichiers markdown...")
documents, paths, raw_contents = collect_markdown_files(ROOT_DIR)
print(f"📄 {len(documents)} fichiers chargés.")

print("📦 Génération des embeddings...")
model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = model.encode(documents, show_progress_bar=True)

# 3. Indexation FAISS
dim = embeddings.shape[1]
index = faiss.IndexFlatL2(dim)
index.add(np.array(embeddings))

# 4. Boucle de questions
while True:
    query = input("\n🔎 Pose ta question : ").strip()
    if not query:
        break

    print("\n🔗 Recherche vectorielle...")
    query_embedding = model.encode([query])
    _, faiss_indices = index.search(np.array(query_embedding), k=5)

    vector_results = [(documents[i], paths[i]) for i in faiss_indices[0]]

    print("🔍 Recherche par mot-clé...")
    keyword_hits = []
    keywords = re.findall(r'\w+', query.lower())
    for i, (path, content) in enumerate(zip(paths, raw_contents)):
        combined = f"{path.lower()} {content.lower()}"
        if all(kw in combined for kw in keywords):
            keyword_hits.append((documents[i], paths[i]))

    # 5. Fusionner résultats (vector d'abord, puis keyword)
    all_results = vector_results + keyword_hits
    seen_paths = set()
    unique_results = []
    for doc, p in all_results:
        if p not in seen_paths:
            unique_results.append((doc, p))
            seen_paths.add(p)

    top_contexts = [doc for doc, _ in unique_results[:3]]
    top_sources = [os.path.relpath(p, ROOT_DIR) for _, p in unique_results[:3]]
    contexte = "\n\n".join(top_contexts)
    fichiers_utilisés = "\n".join(f"- {src}" for src in top_sources)

    # 6. Préparer le prompt
    prompt = (
        f"Contexte :\n{contexte}\n\n"
        f"Question : {query}\n"
        f"Réponds clairement et cite les éléments importants si besoin."
    )

    print("\n🧠 Appel au modèle Ollama...\n")
    res = requests.post(
        "http://localhost:11434/api/generate",
        json={"model": "llama3", "prompt": prompt, "stream": False}
    )

    print("📘 Fichiers utilisés :\n", fichiers_utilisés)
    print("\n🧠 Réponse :\n", res.json()["response"])