{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d797e3ac-c8df-47fc-b349-5d2466d12ca6",
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
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      "\u001b[?25hDownloading mdurl-0.1.2-py3-none-any.whl (10.0 kB)\n",
      "Installing collected packages: sentencepiece, pytz, pydub, faiss-gpu, websockets, tzdata, typing-extensions, tqdm, tomlkit, threadpoolctl, shellingham, semantic-version, scipy, safetensors, ruff, regex, python-multipart, PyMuPDF, protobuf, orjson, mdurl, joblib, h11, fsspec, ffmpy, click, annotated-types, aiofiles, uvicorn, starlette, scikit-learn, pydantic-core, pandas, markdown-it-py, huggingface-hub, httpcore, tokenizers, rich, pydantic, httpx, accelerate, typer, transformers, safehttpx, gradio-client, fastapi, sentence-transformers, gradio\n",
      "  Attempting uninstall: typing-extensions\n",
      "    Found existing installation: typing_extensions 4.4.0\n",
      "    Uninstalling typing_extensions-4.4.0:\n",
      "      Successfully uninstalled typing_extensions-4.4.0\n",
      "  Attempting uninstall: fsspec\n",
      "    Found existing installation: fsspec 2023.4.0\n",
      "    Uninstalling fsspec-2023.4.0:\n",
      "      Successfully uninstalled fsspec-2023.4.0\n",
      "Successfully installed PyMuPDF-1.25.1 accelerate-1.2.1 aiofiles-23.2.1 annotated-types-0.7.0 click-8.1.8 faiss-gpu-1.7.2 fastapi-0.115.6 ffmpy-0.5.0 fsspec-2024.12.0 gradio-5.12.0 gradio-client-1.5.4 h11-0.14.0 httpcore-1.0.7 httpx-0.28.1 huggingface-hub-0.27.1 joblib-1.4.2 markdown-it-py-3.0.0 mdurl-0.1.2 orjson-3.10.14 pandas-2.2.3 protobuf-5.29.3 pydantic-2.10.5 pydantic-core-2.27.2 pydub-0.25.1 python-multipart-0.0.20 pytz-2024.2 regex-2024.11.6 rich-13.9.4 ruff-0.9.1 safehttpx-0.1.6 safetensors-0.5.2 scikit-learn-1.6.1 scipy-1.15.1 semantic-version-2.10.0 sentence-transformers-3.3.1 sentencepiece-0.2.0 shellingham-1.5.4 starlette-0.41.3 threadpoolctl-3.5.0 tokenizers-0.21.0 tomlkit-0.13.2 tqdm-4.67.1 transformers-4.48.0 typer-0.15.1 typing-extensions-4.12.2 tzdata-2024.2 uvicorn-0.34.0 websockets-14.1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install gradio PyMuPDF numpy faiss-gpu transformers sentence-transformers protobuf tokenizers accelerate sentencepiece"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e952b997-7a55-4eae-8b44-3be6a82b1bcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting bitsandbytes\n",
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      "Downloading bitsandbytes-0.45.0-py3-none-manylinux_2_24_x86_64.whl (69.1 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.1/69.1 MB\u001b[0m \u001b[31m41.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: bitsandbytes\n",
      "Successfully installed bitsandbytes-0.45.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install bitsandbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba87e888-bde5-4fc9-816b-a4cc4361d1bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file you can ignore this message\n",
      "You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file you can ignore this message.\n"
     ]
    },
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       "model_id": "9931b1deaeca47309c3ddffc40647368",
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       "model_id": "9067276802da41ac9bdcd0cf439b7887",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return self.fget.__get__(instance, owner)()\n"
     ]
    },
    {
     "data": {
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       "model_id": "c31865eaedec400496e790306cddfd60",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import fitz  # PyMuPDF\n",
    "import numpy as np\n",
    "import faiss\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "from sentence_transformers import SentenceTransformer\n",
    "import torch\n",
    "from transformers import BitsAndBytesConfig\n",
    "\n",
    "\n",
    "model_name = \"ZDPLI/SkinGPTVicunaMerged\"  # Example open-source model\n",
    "quantization_config = BitsAndBytesConfig(load_in_8bit=True)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quantization_config, device_map=\"auto\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f0256261-1610-4b6f-b947-58c398a99bd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Running on local URL:  http://127.0.0.1:7860\n",
      "* Running on public URL: https://e9a1fd35c9941c2e3f.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://e9a1fd35c9941c2e3f.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "model_id": "04ea1db8d4094ed6ba1e997c5631dcad",
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "a92a037d6809413eb47e6bb3a4d1061b",
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      "text/plain": [
       "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "ce812db4cb204caabf396c361295efb5",
       "version_major": 2,
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      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
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    },
    {
     "data": {
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    },
    {
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       "model_id": "ab101967752941abb386512322d8d728",
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     },
     "metadata": {},
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    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "dfbcffd3466a423cb78f639fcf7864af",
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     },
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    },
    {
     "data": {
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       "model_id": "5f8e73e7e4994381a0c45bb9098def94",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9cbd21d1770f4ce59c25bbf4c5ea20d2",
       "version_major": 2,
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import fitz  # PyMuPDF\n",
    "import numpy as np\n",
    "import faiss\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "from sentence_transformers import SentenceTransformer\n",
    "import torch\n",
    "from transformers import BitsAndBytesConfig\n",
    "\n",
    "# Helper functions\n",
    "def extract_text_from_pdf(pdf_file):\n",
    "    \"\"\"Extract text from a PDF file.\"\"\"\n",
    "    text = \"\"\n",
    "    with fitz.open(stream=pdf_file.read(), filetype=\"pdf\") as doc:\n",
    "        for page in doc:\n",
    "            text += page.get_text()\n",
    "    return text\n",
    "\n",
    "def chunk_text(text, chunk_size=1000, chunk_overlap=200):\n",
    "    \"\"\"Split text into overlapping chunks.\"\"\"\n",
    "    chunks = []\n",
    "    for i in range(0, len(text), chunk_size - chunk_overlap):\n",
    "        chunks.append(text[i : i + chunk_size])\n",
    "    return chunks\n",
    "\n",
    "def embed_chunks(chunks, embedder):\n",
    "    \"\"\"Generate embeddings for text chunks.\"\"\"\n",
    "    return embedder.encode(chunks, convert_to_tensor=True)\n",
    "\n",
    "def create_vector_store(embeddings):\n",
    "    \"\"\"Create a FAISS vector store for the embeddings.\"\"\"\n",
    "    dimension = embeddings.shape[1]\n",
    "    index = faiss.IndexFlatL2(dimension)\n",
    "    index.add(embeddings.cpu().numpy().astype(\"float32\"))\n",
    "    return index\n",
    "\n",
    "def retrieve_relevant_chunks(query, index, chunks, embedder, top_k=5):\n",
    "    \"\"\"Retrieve the most relevant chunks for a query.\"\"\"\n",
    "    query_embedding = embedder.encode([query], convert_to_tensor=True).cpu().numpy()\n",
    "    distances, indices = index.search(query_embedding, top_k)\n",
    "    return [chunks[i] for i in indices[0]]\n",
    "\n",
    "def process_query(pdf_file, image, user_query):\n",
    "    \"\"\"Process user query using uploaded PDF and/or image.\"\"\"\n",
    "    embedder = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
    "\n",
    "    # Initialize variables for context\n",
    "    extracted_text = \"\"\n",
    "    chunks = []\n",
    "\n",
    "    if pdf_file is not None:\n",
    "        extracted_text = extract_text_from_pdf(pdf_file)\n",
    "        chunks = chunk_text(extracted_text)\n",
    "        embeddings = embed_chunks(chunks, embedder)\n",
    "        vector_store = create_vector_store(embeddings)\n",
    "        relevant_chunks = retrieve_relevant_chunks(user_query, vector_store, chunks, embedder)\n",
    "    else:\n",
    "        relevant_chunks = []\n",
    "\n",
    "    # Prepare context\n",
    "    context = \" \".join(relevant_chunks)\n",
    "    if image is not None:\n",
    "        context += \" Image information is available for analysis.\"  # Placeholder for image processing integration\n",
    "\n",
    "    # Generate response\n",
    "    input_text = f\"Context: {context}\\n\\nQuestion: {user_query}\\n\\nAnswer:\"\n",
    "    inputs = tokenizer(input_text, return_tensors=\"pt\").to(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    outputs = model.generate(**inputs, max_length=512, num_return_sequences=1)\n",
    "    response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "\n",
    "    return \"\\n\\n\".join(relevant_chunks) if relevant_chunks else \"No relevant PDF content found.\", response\n",
    "\n",
    "# Gradio WebUI\n",
    "with gr.Blocks() as demo:\n",
    "    gr.Markdown(\"# SkinGPT-4 Custom WebUI (Vicuna-based)\")\n",
    "\n",
    "    with gr.Row():\n",
    "        pdf_input = gr.File(label=\"Upload PDF (optional)\", file_types=[\".pdf\"])\n",
    "        image_input = gr.Image(label=\"Upload Image (optional)\")\n",
    "        user_query = gr.Textbox(label=\"Enter your question\")\n",
    "\n",
    "    with gr.Row():\n",
    "        relevant_info = gr.Textbox(label=\"Relevant Information from PDF\", lines=10)\n",
    "        model_response = gr.Textbox(label=\"Model's Response\", lines=10)\n",
    "\n",
    "    submit_button = gr.Button(\"Submit\")\n",
    "\n",
    "    submit_button.click(\n",
    "        process_query, inputs=[pdf_input, image_input, user_query], outputs=[relevant_info, model_response]\n",
    "    )\n",
    "\n",
    "demo.launch(share=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7c65ea7-548f-49df-a0d7-75343b0af67e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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