app.py

import os
import io
import base64
import gc
from huggingface_hub.utils import HfHubHTTPError
from langchain_core.prompts import PromptTemplate
from langchain_huggingface import HuggingFaceEndpoint
import io, base64
from PIL import Image
import torch
import gradio as gr
import spaces
import numpy as np
import pandas as pd
import pymupdf
from PIL import Image
from pypdf import PdfReader
from dotenv import load_dotenv
import shutil
from chromadb.config import Settings
from welcome_text import WELCOME_INTRO

from doctr.io import DocumentFile
from doctr.models import ocr_predictor
from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration

import chromadb
from chromadb.utils import embedding_functions
from chromadb.utils.data_loaders import ImageLoader

from langchain_core.prompts import PromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_huggingface import HuggingFaceEndpoint

from utils import extract_pdfs, extract_images, clean_text, image_to_bytes
from utils import *

# ─────────────────────────────────────────────────────────────────────────────
# Load .env
load_dotenv()
HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
processor = None
vision_model = None
# OCR + multimodal image description setup
ocr_model = ocr_predictor(
    "db_resnet50", "crnn_mobilenet_v3_large", pretrained=True, assume_straight_pages=True
)
processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
vision_model = LlavaNextForConditionalGeneration.from_pretrained(
    "llava-hf/llava-v1.6-mistral-7b-hf",
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True
).to("cuda")


# Add at the top of your module, alongside your other globals
CURRENT_VDB = None


@spaces.GPU()
def get_image_description(image: Image.Image) -> str:
    """
    Lazy-loads the Llava processor + model inside the GPU worker,
    runs captioning, and returns a one-sentence description.
    """
    global processor, vision_model

    # On first call, instantiate + move to CUDA
    if processor is None or vision_model is None:
        processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
        vision_model = LlavaNextForConditionalGeneration.from_pretrained(
            "llava-hf/llava-v1.6-mistral-7b-hf",
            torch_dtype=torch.float16,
            low_cpu_mem_usage=True
        ).to("cuda")

    torch.cuda.empty_cache()
    gc.collect()

    prompt = "[INST] <image>\nDescribe the image in a sentence [/INST]"
    inputs = processor(prompt, image, return_tensors="pt").to("cuda")
    output = vision_model.generate(**inputs, max_new_tokens=100)
    return processor.decode(output[0], skip_special_tokens=True)

# Vector DB setup
# at top of file, alongside your other imports
from chromadb.utils import embedding_functions
from chromadb.utils.data_loaders import ImageLoader
import chromadb
from langchain.text_splitter import RecursiveCharacterTextSplitter
from utils import image_to_bytes  # your helper

# 1) Create one shared embedding function (defaulting to All-MiniLM-L6-v2, 384-dim)
SHARED_EMB_FN = embedding_functions.SentenceTransformerEmbeddingFunction(
    model_name="all-MiniLM-L6-v2"
)

def get_vectordb(text: str, images: list[Image.Image], img_names: list[str]):
    """
    Build an in-memory ChromaDB instance with two collections:
      • text_db  (chunks of the PDF text)
      • image_db (image descriptions + raw image bytes)
    Returns the Chroma client for later querying.
    """
    # ——— 1) Init & wipe old ————————————————
    client = chromadb.EphemeralClient()
    for col in ("text_db", "image_db"):
        if col in [c.name for c in client.list_collections()]:
            client.delete_collection(col)

    # ——— 2) Create fresh collections —————————
    text_col = client.get_or_create_collection(
        name="text_db",
        embedding_function=SHARED_EMB_FN,
        data_loader=ImageLoader(),   # loader only matters for images, benign here
    )
    img_col = client.get_or_create_collection(
        name="image_db",
        embedding_function=SHARED_EMB_FN,
        metadata={"hnsw:space": "cosine"},
        data_loader=ImageLoader(),
    )

    # ——— 3) Add images if any ———————————————
    if images:
        descs = []
        metas = []
        for idx, img in enumerate(images):
            # build one-line caption (or fallback)
            try:
                caption = get_image_description(img)
            except Exception:
                caption = "⚠️ could not describe image"
            descs.append(f"{img_names[idx]}: {caption}")
            metas.append({"image": image_to_bytes(img)})

        img_col.add(
            ids=[str(i) for i in range(len(images))],
            documents=descs,
            metadatas=metas,
        )

    # ——— 4) Chunk & add text ———————————————
    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    docs = splitter.create_documents([text])
    text_col.add(
        ids=[str(i) for i in range(len(docs))],
        documents=[d.page_content for d in docs],
    )

    return client



# Text extraction
def result_to_text(result, as_text=False):
    pages = []
    for pg in result.pages:
        txt = " ".join(w.value for block in pg.blocks for line in block.lines for w in line.words)
        pages.append(clean_text(txt))
    return "\n\n".join(pages) if as_text else pages

OCR_CHOICES = {
    "db_resnet50 + crnn_mobilenet_v3_large": ("db_resnet50", "crnn_mobilenet_v3_large"),
    "db_resnet50 + crnn_resnet31":          ("db_resnet50", "crnn_resnet31"),
}

@spaces.GPU()
def extract_data_from_pdfs(
    docs: list[str],
    session: dict,
    include_images: str,    # "Include Images" or "Exclude Images"
    do_ocr: str,            # "Get Text With OCR" or "Get Available Text Only"
    ocr_choice: str,        # key into OCR_CHOICES
    vlm_choice: str,        # HF repo ID for LlavaNext
    progress=gr.Progress()
):
    """
    1) (Optional) OCR setup
    2) Vision+Lang model setup & monkey-patch get_image_description
    3) Extract text & images
    4) Build and stash vector DB in CURRENT_VDB
    """
    if not docs:
        raise gr.Error("No documents to process")

    # 1) OCR pipeline if requested
    if do_ocr == "Get Text With OCR":
        db_m, crnn_m = OCR_CHOICES[ocr_choice]
        local_ocr = ocr_predictor(db_m, crnn_m, pretrained=True, assume_straight_pages=True)
    else:
        local_ocr = None

    # 2) Vision–language model
    proc = LlavaNextProcessor.from_pretrained(vlm_choice)
    vis = (
        LlavaNextForConditionalGeneration
        .from_pretrained(vlm_choice, torch_dtype=torch.float16, low_cpu_mem_usage=True)
        .to("cuda")
    )

    # Monkey-patch our pipeline for image captions
    def describe(img: Image.Image) -> str:
        torch.cuda.empty_cache()
        gc.collect()
        prompt = "[INST] <image>\nDescribe the image in a sentence [/INST]"
        inputs = proc(prompt, img, return_tensors="pt").to("cuda")
        output = vis.generate(**inputs, max_new_tokens=100)
        return proc.decode(output[0], skip_special_tokens=True)

    global get_image_description, CURRENT_VDB
    get_image_description = describe

    # 3) Extract text + images
    progress(0.2, "Extracting text and images…")
    all_text = ""
    images, names = [], []

    for path in docs:
        if local_ocr:
            pdf = DocumentFile.from_pdf(path)
            res = local_ocr(pdf)
            all_text += result_to_text(res, as_text=True) + "\n\n"
        else:
            txt = PdfReader(path).pages[0].extract_text() or ""
            all_text += txt + "\n\n"

        if include_images == "Include Images":
            imgs = extract_images([path])
            images.extend(imgs)
            names.extend([os.path.basename(path)] * len(imgs))

    # 4) Build + store the vector DB
    progress(0.6, "Indexing in vector DB…")
    CURRENT_VDB = get_vectordb(all_text, images, names)

    session["processed"] = True
    sample_imgs = images[:4] if include_images == "Include Images" else []

    # ─── return *exactly four* picklable outputs ───
    return (
        session,            # gr.State: so UI knows we're ready
        all_text[:2000] + "...",  # preview text
        sample_imgs,        # preview images
        "<h3>Done!</h3>"    # Done message
    )



# Chat function
def conversation(
    session: dict,
    question: str,
    num_ctx: int,
    img_ctx: int,
    history: list,
    temp: float,
    max_tok: int,
    model_id: str
):
    """
    Uses the global CURRENT_VDB (set by extract_data_from_pdfs) to answer.
    """
    global CURRENT_VDB
    if not session.get("processed") or CURRENT_VDB is None:
        raise gr.Error("Please extract data first")

    llm = HuggingFaceEndpoint(
        repo_id=model_id,
        temperature=temp,
        max_new_tokens=max_tok,
        huggingfacehub_api_token=HF_TOKEN
    )

    # 1) Text retrieval
    text_col = CURRENT_VDB.get_collection("text_db")
    docs = text_col.query(
        query_texts=[question],
        n_results=int(num_ctx),
        include=["documents"]
    )["documents"][0]

    # 2) Image retrieval
    img_col = CURRENT_VDB.get_collection("image_db")
    img_q = img_col.query(
        query_texts=[question],
        n_results=int(img_ctx),
        include=["metadatas", "documents"]
    )
    img_descs = img_q["documents"][0] or ["No images found"]
    images = []
    for meta in img_q["metadatas"][0]:
        b64 = meta.get("image", "")
        try:
            images.append(Image.open(io.BytesIO(base64.b64decode(b64))))
        except:
            pass
    img_desc = "\n".join(img_descs)

    # 3) Build prompt & call LLM
    prompt = PromptTemplate(
        template="""
Context:
{text}

Included Images:
{img_desc}

Question:
{q}

Answer:
""",
        input_variables=["text", "img_desc", "q"],
    )
    user_input = prompt.format(
        text="\n\n".join(docs),
        img_desc=img_desc,
        q=question
    )

    try:
        answer = llm.invoke(user_input)
    except HfHubHTTPError as e:
        if e.response.status_code == 404:
            answer = f"❌ Model `{model_id}` not hosted on HF Inference API."
        else:
            answer = f"⚠️ HF API error: {e}"
    except Exception as e:
        answer = f"⚠️ Unexpected error: {e}"

    new_history = history + [
        {"role": "user",      "content": question},
        {"role": "assistant", "content": answer}
    ]
    return new_history, docs, images




# ─────────────────────────────────────────────────────────────────────────────
# Gradio UI
CSS = """
footer {visibility:hidden;}
"""

MODEL_OPTIONS = [
    "HuggingFaceH4/zephyr-7b-beta",
    "mistralai/Mistral-7B-Instruct-v0.2",
    "openchat/openchat-3.5-0106",
    "google/gemma-7b-it",
    "deepseek-ai/deepseek-llm-7b-chat",
    "microsoft/Phi-3-mini-4k-instruct",
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    "Qwen/Qwen1.5-7B-Chat",
    "tiiuae/falcon-7b-instruct",              # Falcon 7B Instruct
    "bigscience/bloomz-7b1",                  # BLOOMZ 7B
    "facebook/opt-2.7b",  
]

with gr.Blocks(css=CSS, theme=gr.themes.Soft()) as demo:
    # State to track that extraction completed (and carry any metadata)
    session_state = gr.State({})

    # ─── Welcome Screen ─────────────────────────────────────────────
    with gr.Column(visible=True) as welcome_col:
        gr.Markdown(
            f"<div style='text-align: center'>\n{WELCOME_INTRO}\n</div>",
            elem_id="welcome_md"
        )
        start_btn = gr.Button("🚀 Start")

    # ─── Main App (hidden until Start is clicked) ───────────────────
    with gr.Column(visible=False) as app_col:
        gr.Markdown("## 📚 Multimodal Chat-PDF Playground")

        # We need to capture the extract‐event so we can chain the “show chat tab” later
        extract_event = None

        with gr.Tabs() as tabs:
            # ── Tab 1: Upload & Extract ───────────────────────────────
            with gr.TabItem("1. Upload & Extract"):
                docs = gr.File(
                    file_count="multiple",
                    file_types=[".pdf"],
                    label="Upload PDFs"
                )
                include_dd = gr.Radio(
                    ["Include Images", "Exclude Images"],
                    value="Exclude Images",
                    label="Images"
                )
                ocr_radio = gr.Radio(
                    ["Get Text With OCR", "Get Available Text Only"],
                    value="Get Available Text Only",
                    label="OCR"
                )
                ocr_dd = gr.Dropdown(
                    choices=list(OCR_CHOICES.keys()),
                    value=list(OCR_CHOICES.keys())[0],
                    label="OCR Model"
                )
                vlm_dd = gr.Dropdown(
                    choices=[
                        "llava-hf/llava-v1.6-mistral-7b-hf",
                        "llava-hf/llava-v1.5-mistral-7b"
                    ],
                    value="llava-hf/llava-v1.6-mistral-7b-hf",
                    label="Vision-Language Model"
                )
                extract_btn = gr.Button("Extract")
                preview_text = gr.Textbox(
                    lines=10,
                    label="Sample Text",
                    interactive=False
                )
                preview_img = gr.Gallery(
                    label="Sample Images",
                    rows=2,
                    value=[]
                )
                preview_html = gr.HTML()

                # Kick off extraction and capture the event
                extract_event = extract_btn.click(
                    fn=extract_data_from_pdfs,
                    inputs=[
                        docs,
                        session_state,
                        include_dd,
                        ocr_radio,
                        ocr_dd,
                        vlm_dd
                    ],
                    outputs=[
                        session_state,   # sets session["processed"]=True
                        preview_text,    # shows first bits of text
                        preview_img,     # shows first images
                        preview_html     # shows “<h3>Done!</h3>”
                    ]
                )

            # ── Tab 2: Chat (initially hidden) ──────────────────────────
            with gr.TabItem("2. Chat", visible=False) as chat_tab:
                with gr.Row():
                    with gr.Column(scale=3):
                        chat = gr.Chatbot(type="messages", label="Chat")
                        msg  = gr.Textbox(
                            placeholder="Ask about your PDF...",
                            label="Your question"
                        )
                        send = gr.Button("Send")
                    with gr.Column(scale=1):
                        model_dd = gr.Dropdown(
                            MODEL_OPTIONS,
                            value=MODEL_OPTIONS[0],
                            label="Choose Chat Model"
                        )
                        num_ctx = gr.Slider(1, 20, value=3, label="Text Contexts")
                        img_ctx = gr.Slider(1, 10, value=2, label="Image Contexts")
                        temp    = gr.Slider(0.1, 1.0, step=0.1, value=0.4, label="Temperature")
                        max_tok = gr.Slider(10, 1000, step=10, value=200, label="Max Tokens")

                send.click(
                    fn=conversation,
                    inputs=[
                        session_state,
                        msg,
                        num_ctx,
                        img_ctx,
                        chat,
                        temp,
                        max_tok,
                        model_dd
                    ],
                    outputs=[
                        chat,
                        gr.Dataframe(),   # shows retrieved text chunks
                        gr.Gallery(label="Relevant Images", rows=2, value=[])
                    ]
                )

        # After both tabs are defined, chain the “unhide chat tab” event
        extract_event.then(
            fn=lambda: gr.update(visible=True),
            inputs=[],
            outputs=[chat_tab]
        )

        gr.HTML("<center>Made with ❤️ by Zamal</center>")

    # ─── Wire the Start button ───────────────────────────────────────
    start_btn.click(
        fn=lambda: (gr.update(visible=False), gr.update(visible=True)),
        inputs=[],
        outputs=[welcome_col, app_col]
    )

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