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 gradio as gr import torch import gradio as gr import numpy as np import pandas as pd import pymupdf from PIL import Image from pypdf import PdfReader from dotenv import load_dotenv 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") # 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("cpu") def get_image_description(image: Image.Image) -> str: """Generate a one-sentence description via LlavaNext.""" torch.cuda.empty_cache() gc.collect() prompt = "[INST] \nDescribe the image in a sentence [/INST]" inputs = processor(prompt, image, return_tensors="pt").to("cpu") 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"), } def extract_data_from_pdfs( docs, session, include_images, # "Include Images" or "Exclude Images" do_ocr, # "Get Text With OCR" or "Get Available Text Only" ocr_choice, # key into OCR_CHOICES vlm_choice, # HF repo ID for LlavaNext progress=gr.Progress() ): """ 1) Dynamically instantiate the chosen OCR pipeline (if any) 2) Dynamically instantiate the chosen vision‐language model 3) Override the global get_image_description to use that model for captions 4) Extract text & images, index into ChromaDB """ if not docs: raise gr.Error("No documents to process") # ——— 1) Set up OCR 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) Set up 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("cpu") # ——— 3) Monkey‐patch global get_image_description ———— def describe(img: Image.Image) -> str: torch.cuda.empty_cache(); gc.collect() prompt = "[INST] \nDescribe the image in a sentence [/INST]" inputs = proc(prompt, img, return_tensors="pt").to("cpu") output = vis.generate(**inputs, max_new_tokens=100) return proc.decode(output[0], skip_special_tokens=True) global get_image_description get_image_description = describe # ——— 4) 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 += "\n\n" + txt + "\n\n" if include_images == "Include Images": imgs = extract_images([path]) images.extend(imgs) names.extend([os.path.basename(path)] * len(imgs)) # ——— 5) Index into vector DB ———————————————— progress(0.6, "Indexing in vector DB…") vdb = get_vectordb(all_text, images, names) session["processed"] = True sample_imgs = images[:4] if include_images == "Include Images" else [] return ( vdb, session, gr.Row(visible=True), all_text[:2000] + "...", sample_imgs, "

Done!

" ) # Chat function def conversation( vdb, question: str, num_ctx, img_ctx, history: list, temp: float, max_tok: int, model_id: str ): # 0) Cast the context sliders to ints num_ctx = int(num_ctx) img_ctx = int(img_ctx) # 1) Guard: must have extracted first if vdb is None: raise gr.Error("Please extract data first") # 2) Instantiate the chosen HF endpoint llm = HuggingFaceEndpoint( repo_id=model_id, temperature=temp, max_new_tokens=max_tok, huggingfacehub_api_token=HF_TOKEN ) # 3) Query text collection text_col = vdb.get_collection("text_db") docs = text_col.query( query_texts=[question], n_results=num_ctx, # now an int include=["documents"] )["documents"][0] # 4) Query image collection img_col = vdb.get_collection("image_db") img_q = img_col.query( query_texts=[question], n_results=img_ctx, # now an int include=["metadatas", "documents"] ) # … rest unchanged … images, img_descs = [], img_q["documents"][0] or ["No images found"] 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) # 5) Build prompt prompt = PromptTemplate( template=""" Context: {text} Included Images: {img_desc} Question: {q} Answer: """, input_variables=["text", "img_desc", "q"], ) context = "\n\n".join(docs) user_input = prompt.format(text=context, img_desc=img_desc, q=question) # 6) Call the model with error handling 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}" # 7) Append to history new_history = history + [ {"role":"user", "content": question}, {"role":"assistant","content": answer} ] # 8) Return updated history, docs, images 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: vdb_state = gr.State() session_state = gr.State({}) # ─── Welcome Screen ───────────────────────────────────────────── with gr.Column(visible=True) as welcome_col: gr.Markdown( f"
\n{WELCOME_INTRO}\n
", 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") with gr.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_dd = gr.Dropdown( choices=[ "db_resnet50 + crnn_mobilenet_v3_large", "db_resnet50 + crnn_resnet31" ], value="db_resnet50 + crnn_mobilenet_v3_large", 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=[]) extract_btn.click( extract_data_from_pdfs, inputs=[ docs, session_state, include_dd, gr.Radio( ["Get Text With OCR", "Get Available Text Only"], value="Get Available Text Only", label="OCR" ), ocr_dd, vlm_dd ], outputs=[ vdb_state, session_state, gr.Row(visible=False), preview_text, preview_img, gr.HTML() ] ) # Tab 2: Chat with gr.TabItem("2. Chat"): 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( conversation, inputs=[ vdb_state, msg, num_ctx, img_ctx, chat, temp, max_tok, model_dd ], outputs=[ chat, gr.Dataframe(), gr.Gallery(label="Relevant Images", rows=2, value=[]) ] ) # Footer inside app_col gr.HTML("
Made with ❤️ by Zamal
") # ─── 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()