src/logic.py

import os, glob, json, faiss, numpy as np
from sentence_transformers import SentenceTransformer
from transformers import pipeline
from groq import Groq
from src.config import *

# Ensure directories exist
os.makedirs(INDEX_DIR, exist_ok=True)

# Initialize models
embedder = SentenceTransformer(EMBEDDING_MODEL)
summarizer = pipeline("summarization", model=SUMMARIZER_MODEL)
client = Groq(api_key=os.getenv("GROQ_API_KEY"))

# --- Token Counter ---
try:
    import tiktoken
    enc = tiktoken.encoding_for_model("gpt-3.5-turbo")
    def count_tokens(text): return len(enc.encode(text))
except Exception:
    def count_tokens(text): return len(text) // 4

# --- Build Index ---
def build_index():
    index = faiss.IndexFlatIP(384)
    meta = []

    def chunk_text(text, size=800, overlap=120):
        chunks = []
        i = 0
        while i < len(text):
            chunks.append(text[i:i+size].strip())
            i += size - overlap
        return chunks

    for domain_dir in glob.glob(f"{RAW_DIR}/*"):
        domain = os.path.basename(domain_dir)
        for path in glob.glob(f"{domain_dir}/*.txt"):
            with open(path, encoding="utf-8") as f:
                text = f.read()
            chunks = chunk_text(text)
            vecs = embedder.encode(chunks, normalize_embeddings=True)
            index.add(np.array(vecs).astype("float32"))
            for ch in chunks:
                meta.append({"domain": domain, "text": ch, "source": os.path.basename(path)})
            print(f"✅ Indexed {domain}/{os.path.basename(path)} ({len(chunks)} chunks)")

    faiss.write_index(index, INDEX_PATH)
    json.dump(meta, open(META_PATH, "w"))
    print(f"🎉 Index built: {len(meta)} chunks total.")
    return index, meta

# Load or build index
if not os.path.exists(INDEX_PATH):
    index, meta = build_index()
else:
    index = faiss.read_index(INDEX_PATH)
    meta = json.load(open(META_PATH))

# --- Retrieval ---
def retrieve_text(query, topk=TOP_K_RESULTS):
    qvec = embedder.encode([query], normalize_embeddings=True).astype("float32")
    D, I = index.search(qvec, topk)
    return [meta[i] for i in I[0]]

# --- Token limiter ---
def trim_to_token_limit(text, max_tokens=MAX_TOKENS):
    tokens = count_tokens(text)
    if tokens > max_tokens:
        print(f"⚠️ Context too long ({tokens}). Trimming...")
        cutoff_ratio = max_tokens / tokens
        text = text[:int(len(text) * cutoff_ratio)]
    return text

# --- Main Answer Generator ---
def generate_answer(query, mode):
    retrieved = retrieve_text(query)
    combined = " ".join([r["text"] for r in retrieved])
    safe_context = trim_to_token_limit(combined)

    if mode == "Quick Summary (Offline)":
        summary = summarizer(safe_context, max_length=180, min_length=60, do_sample=False)[0]["summary_text"]
    else:
        prompt = f"""
You are MindMesh, a cross-domain reasoning assistant.
Question: {query}
Context: {safe_context}
Synthesize a precise and insightful answer across disciplines.
"""
        try:
            response = client.chat.completions.create(
                model=PRIMARY_GROQ_MODEL,
                messages=[{"role": "user", "content": prompt}],
            )
            summary = response.choices[0].message.content.strip()
        except Exception as e:
            try:
                response = client.chat.completions.create(
                    model=FALLBACK_GROQ_MODEL,
                    messages=[{"role": "user", "content": prompt}],
                )
                summary = response.choices[0].message.content.strip()
            except Exception as e2:
                summary = f"⚠️ Groq API error: {str(e2)}"

    md = f"## 🧭 Synthesized Insight\n{summary}\n\n---\n### 🔍 Source Highlights\n"
    for r in retrieved:
        md += f"**{r['domain'].title()} — {r['source']}**  \n{r['text'][:300]}...\n\n"
    return md

# --- Rebuild Index with Feedback ---
def rebuild():
    yield "⚙️ Rebuilding FAISS index... please wait ⏳"
    build_index()
    yield "✅ Index rebuilt successfully! (FAISS + metadata updated)"