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 AutoProcessor, AutoModelForVision2Seq
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 = "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("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("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("DOCX read error:", path, e)
        return []

# ---------------- Embedding ----------------
def embed_all():
    try:
        embedder = SentenceTransformer("all-MiniLM-L6-v2")
        embedder.eval()
        client = chromadb.PersistentClient(path=CHROMA_PATH)

        try:
            client.delete_collection(COLLECTION_NAME)
        except:
            pass

        collection = client.get_or_create_collection(COLLECTION_NAME)

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

        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) >= 32:  # Increased batch size for efficiency
                        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"Embedded {len(ids)} chunks.")
        return collection, embedder

    except Exception as e:
        print("Embedding startup failed:", e)
        return None, None

# ---------------- Model Setup ----------------
def load_model():
    try:
        processor = AutoProcessor.from_pretrained(MODEL_ID, token=HF_TOKEN)
        model = AutoModelForVision2Seq.from_pretrained(MODEL_ID, token=HF_TOKEN).to(device)
        return model, processor
    except Exception as e:
        print("Model loading failed:", e)
        return None, None

def ask_model(question, context, model, processor):
    prompt = f"""Use only the following context to answer. If uncertain, say \"I don't know.\"

<context>
{context}
</context>

Q: {question}
A:"""
    inputs = processor(prompt, return_tensors="pt").to(device)
    output = model.generate(**inputs)
    return processor.decode(output[0], skip_special_tokens=True)

# ---------------- Query ----------------
def get_answer(question):
    if not embedder or not db or not model:
        return "System not ready. Try again after initialization."
    try:
        query_emb = embedder.encode(question, convert_to_tensor=True)
        results = db.query(query_texts=[question], n_results=MAX_CONTEXT_CHUNKS)
        context = "\n\n".join(results["documents"][0])
        return ask_model(question, context, model, processor)
    except Exception as e:
        print("Query error:", e)
        return f"Error: {e}"

# ---------------- UI ----------------
with gr.Blocks() as demo:
    gr.Markdown("## 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)
    ask.click(fn=get_answer, inputs=question, outputs=answer)

# Startup Initialization
embedder = None
model = None
processor = None

try:
    db, embedder = embed_all()
except Exception as e:
    print("❌ Embedding failed:", e)

try:
    model, processor = load_model()
except Exception as e:
    print("❌ Model load failed:", e)

# Launch
if __name__ == "__main__":
    demo.launch(share=False)