app.py

import os
import fitz
import json
import gradio as gr
import pytesseract
import chromadb
import torch
import nltk
import traceback
import docx2txt
from PIL import Image
from io import BytesIO
from tqdm import tqdm
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from sentence_transformers import SentenceTransformer, util
from nltk.tokenize import sent_tokenize

# Ensure punkt is downloaded
try:
    nltk.data.find("tokenizers/punkt")
except LookupError:
    nltk.download("punkt")

# Configuration
HF_TOKEN = os.getenv("HF_TOKEN")
MANUALS_DIR = "Manuals"
CHROMA_PATH = "chroma_store"
COLLECTION_NAME = "manual_chunks"
CHUNK_SIZE = 750
CHUNK_OVERLAP = 100
MAX_CONTEXT_CHUNKS = 3
MODEL_ID = "ibm-granite/granite-vision-3.2-2b"

# Device selection
device = "cuda" if torch.cuda.is_available() else "cpu"

# ---------------- Text Helpers ----------------
def clean(text):
    return "\n".join([line.strip() for line in text.splitlines() if line.strip()])

def split_sentences(text):
    try:
        return sent_tokenize(text)
    except:
        print("\u26a0\ufe0f Tokenizer fallback: simple split.")
        return text.split(". ")

def split_chunks(sentences, max_tokens=CHUNK_SIZE, overlap=CHUNK_OVERLAP):
    chunks = []
    current_chunk, length = [], 0

    for sent in sentences:
        words = sent.split()
        if length + len(words) > max_tokens and current_chunk:
            chunks.append(" ".join(current_chunk))
            current_chunk = current_chunk[-overlap:]
            length = sum(len(s.split()) for s in current_chunk)
        current_chunk.append(sent)
        length += len(words)

    if current_chunk:
        chunks.append(" ".join(current_chunk))
    return chunks

# ---------------- File Readers ----------------
def extract_pdf_text(path):
    chunks = []
    try:
        doc = fitz.open(path)
        for i, page in enumerate(doc):
            text = page.get_text().strip()
            if not text:
                img = Image.open(BytesIO(page.get_pixmap(dpi=300).tobytes("png")))
                text = pytesseract.image_to_string(img)
            chunks.append((path, i + 1, clean(text)))
    except Exception as e:
        print("\u274c PDF read error:", path, e)
    return chunks

def extract_docx_text(path):
    try:
        return [(path, 1, clean(docx2txt.process(path)))]
    except Exception as e:
        print("\u274c DOCX read error:", path, e)
        return []

# ---------------- Embedding ----------------
def embed_all():
    try:
        embedder = SentenceTransformer("all-MiniLM-L6-v2")
        embedder.eval()
    except Exception as e:
        print("\u274c Failed to load SentenceTransformer:", e)
        return None, None

    try:
        client = chromadb.PersistentClient(path=CHROMA_PATH)
        client.delete_collection(COLLECTION_NAME)
        collection = client.get_or_create_collection(COLLECTION_NAME)
    except Exception as e:
        print("\u274c Failed to initialize ChromaDB:", e)
        return None, None

    docs, ids, metas = [], [], []
    print("\ud83d\udcc4 Processing manuals...")

    try:
        for fname in os.listdir(MANUALS_DIR):
            fpath = os.path.join(MANUALS_DIR, fname)
            if fname.lower().endswith(".pdf"):
                pages = extract_pdf_text(fpath)
            elif fname.lower().endswith(".docx"):
                pages = extract_docx_text(fpath)
            else:
                continue

            for path, page, text in pages:
                for i, chunk in enumerate(split_chunks(split_sentences(text))):
                    chunk_id = f"{fname}::{page}::{i}"
                    docs.append(chunk)
                    ids.append(chunk_id)
                    metas.append({"source": fname, "page": page})

                    if len(docs) >= 16:
                        embs = embedder.encode(docs).tolist()
                        collection.add(documents=docs, ids=ids, metadatas=metas, embeddings=embs)
                        docs, ids, metas = [], [], []

        if docs:
            embs = embedder.encode(docs).tolist()
            collection.add(documents=docs, ids=ids, metadatas=metas, embeddings=embs)

        print(f"\u2705 Embedded {len(ids)} chunks.")
        return collection, embedder

    except Exception as e:
        print("\u274c Error during embedding:", e)
        return None, None

# ---------------- Model Setup ----------------
def load_model():
    try:
        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=HF_TOKEN)
        model = AutoModelForCausalLM.from_pretrained(
            MODEL_ID,
            token=HF_TOKEN,
            device_map="auto" if torch.cuda.is_available() else None,
            torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
        ).to(device)
        pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
        return pipe, tokenizer
    except Exception as e:
        print("\u274c Failed to load model:", e)
        return None, None

# ---------------- QA Logic ----------------
def ask_model(question, context, pipe, tokenizer):
    prompt = f"""Use only the following context to answer. If uncertain, say \"I don't know.\"

<context>
{context}
</context>

Q: {question}
A:"""
    output = pipe(prompt, max_new_tokens=512)[0]["generated_text"]
    return output.split("A:")[-1].strip()

def get_answer(question):
    if not all([embedder, db, model_pipe, model_tokenizer]):
        return "\u274c System not initialized. Check logs or try restarting the app."
    try:
        results = db.query(query_texts=[question], n_results=MAX_CONTEXT_CHUNKS)
        context = "\n\n".join(results["documents"][0])
        return ask_model(question, context, model_pipe, model_tokenizer)
    except Exception as e:
        print("\u274c Query error:", e)
        return f"Error: {e}"

# ---------------- UI ----------------
with gr.Blocks() as demo:
    gr.Markdown("## \ud83e\udd16 SmartManuals-AI (Granite 3.2-2B)")
    with gr.Row():
        question = gr.Textbox(label="Ask your question")
        ask = gr.Button("Ask")
    answer = gr.Textbox(label="Answer", lines=8)
    status = gr.Markdown(visible=False)

    def wrapped_get_answer(q):
        ans = get_answer(q)
        return ans, ""  # hide status after success

    ask.click(fn=wrapped_get_answer, inputs=question, outputs=[answer, status])

    # Show status on startup error
    if not all([embedder, db, model_pipe, model_tokenizer]):
        status.visible = True
        status.value = "\u26a0\ufe0f Initialization failed. Check logs or your HF_TOKEN."

# Embed + Load Model at Startup
try:
    db, embedder = embed_all()
except Exception as e:
    print("\u274c Embedding failed:", e)
    db, embedder = None, None

try:
    model_pipe, model_tokenizer = load_model()
except Exception as e:
    print("\u274c Model loading failed:", e)
    model_pipe, model_tokenizer = None, None

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