MedQA_Llama3_1_8B_RAG_gradioDemo.ipynb

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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.4/177.4 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m31.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.5/85.5 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.5/49.5 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n",
            "grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.20.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
            "Mounted at /content/drive\n",
            "==((====))==  Unsloth 2025.1.5: Fast Llama patching. Transformers: 4.47.1.\n",
            "   \\\\   /|    GPU: NVIDIA L4. Max memory: 22.168 GB. Platform: Linux.\n",
            "O^O/ \\_/ \\    Torch: 2.5.1+cu121. CUDA: 8.9. CUDA Toolkit: 12.1. Triton: 3.1.0\n",
            "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
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            "Unsloth 2025.1.5 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
          ]
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              "Generating train split: 0 examples [00:00, ? examples/s]"
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              "model_id": "426af7ec31014e9d818dcee34e450de0"
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              "Generating validation split: 0 examples [00:00, ? examples/s]"
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        {
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          "text": [
            "<ipython-input-1-b8ad60a15009>:42: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-huggingface package and should be used instead. To use it run `pip install -U :class:`~langchain-huggingface` and import as `from :class:`~langchain_huggingface import HuggingFaceEmbeddings``.\n",
            "  embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n"
          ]
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          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "* Running on public URL: https://0a8af4eb40e1238229.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"
          ]
        },
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              "<div><iframe src=\"https://0a8af4eb40e1238229.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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          "execution_count": 1
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      ],
      "source": [
        "# Install required libraries\n",
        "!pip install unsloth datasets langchain faiss-gpu transformers sentence-transformers langchain-community gradio -q\n",
        "\n",
        "# Import necessary modules\n",
        "import torch\n",
        "import gradio as gr\n",
        "from datasets import load_dataset\n",
        "from unsloth import FastLanguageModel\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.llms.base import LLM\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.chains import RetrievalQA\n",
        "from typing import List, Any, Optional\n",
        "from pydantic import PrivateAttr\n",
        "\n",
        "# Mount Google Drive for the model\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "drive_path = \"/content/drive/My Drive/MedQA-Llama3.1-8B_LoRA_Model/lora_model\"\n",
        "\n",
        "# Load LoRA-tuned Llama model\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name=drive_path,\n",
        "    max_seq_length=2048,\n",
        "    dtype=None,\n",
        "    load_in_4bit=True,\n",
        "    device_map=\"auto\"\n",
        ")\n",
        "FastLanguageModel.for_inference(model)\n",
        "\n",
        "# Load PubMedQA dataset\n",
        "dataset = load_dataset(\"bigbio/pubmed_qa\", name=\"pubmed_qa_labeled_fold0_source\", split=\"train\", trust_remote_code=True)\n",
        "docs = [{\"id\": f\"pubmed_qa_train_{i}\", \"text\": ex[\"LONG_ANSWER\"] or \"\"} for i, ex in enumerate(dataset)]\n",
        "\n",
        "# Split dataset into chunks\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
        "chunks = [{\"id\": f\"{d['id']}_chunk_{idx}\", \"text\": chunk} for d in docs for idx, chunk in enumerate(text_splitter.split_text(d[\"text\"]))]\n",
        "\n",
        "# Create FAISS vector store\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
        "texts = [x[\"text\"] for x in chunks]\n",
        "metadatas = [{\"source\": x[\"id\"]} for x in chunks]\n",
        "db = FAISS.from_texts(texts, embedding=embeddings, metadatas=metadatas)\n",
        "\n",
        "# Custom LLM class\n",
        "class LoRAMedicalLLM(LLM):\n",
        "    _model: Any = PrivateAttr()\n",
        "    _tokenizer: Any = PrivateAttr()\n",
        "    max_new_tokens: int = 512\n",
        "    device: str = \"cuda\"\n",
        "\n",
        "    def __init__(self, model, tokenizer, max_new_tokens=256, device=\"cuda\", **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self._model = model\n",
        "        self._tokenizer = tokenizer\n",
        "        self.max_new_tokens = max_new_tokens\n",
        "        self.device = device\n",
        "\n",
        "    @property\n",
        "    def _llm_type(self) -> str:\n",
        "        return \"lora-medical-llm\"\n",
        "\n",
        "    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:\n",
        "        inp = self._tokenizer([prompt], return_tensors=\"pt\", padding=True, truncation=True, max_length=2048).to(self.device)\n",
        "        with torch.no_grad():\n",
        "            out_toks = self._model.generate(**inp, max_new_tokens=self.max_new_tokens, use_cache=True)\n",
        "        return self._tokenizer.decode(out_toks[0], skip_special_tokens=True)\n",
        "\n",
        "llm = LoRAMedicalLLM(model=model, tokenizer=tokenizer)\n",
        "\n",
        "# Define prompt and RAG chain\n",
        "prompt_template = \"\"\"You are a medical QA system.\n",
        "Use the following context to answer the question concisely without repeating the context or any lines in the final answer:\n",
        "{context}\n",
        "\n",
        "Question: {question}\n",
        "\n",
        "Answer:\n",
        "\"\"\"\n",
        "prompt = PromptTemplate(template=prompt_template, input_variables=[\"context\", \"question\"])\n",
        "retriever = db.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": 3})\n",
        "rag = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=retriever, chain_type_kwargs={\"prompt\": prompt})\n",
        "\n",
        "# Define the function for Gradio interface\n",
        "def medical_qa(query):\n",
        "    response = rag.invoke({\"query\": query})\n",
        "    raw_answer = response[\"result\"]\n",
        "    final_answer = raw_answer.split(\"Answer:\")[-1].strip() if \"Answer:\" in raw_answer else raw_answer.strip()\n",
        "\n",
        "    # Retrieve sources\n",
        "    retrieved_docs = retriever.get_relevant_documents(query)\n",
        "    sources = \"\\n\\n\".join([f\"Source {i+1}: {d.page_content}\" for i, d in enumerate(retrieved_docs)])\n",
        "\n",
        "    return f\"Answer:\\n{final_answer}\\n\\nSources:\\n{sources}\"\n",
        "\n",
        "# Set up Gradio interface\n",
        "interface = gr.Interface(\n",
        "    fn=medical_qa,\n",
        "    inputs=gr.Textbox(label=\"Enter your medical question\"),\n",
        "    outputs=gr.Textbox(label=\"Response and Sources\"),\n",
        "    title=\"Medical QA System\",\n",
        "    description=\"Ask any medical question, and get a concise answer along with relevant sources.\"\n",
        ")\n",
        "\n",
        "# Launch Gradio app\n",
        "interface.launch(share=True)\n"
      ]
    }
  ]
}