AnmolRSrivastava-(Part2)-Interactive_QA_Bot_Interface.ipynb

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          "text": [
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            "Installing collected packages: pydub, websockets, watchdog, tomlkit, tenacity, smmap, semantic-version, ruff, python-multipart, PyPDF2, orjson, h11, ffmpy, faiss-cpu, aiofiles, uvicorn, starlette, pydeck, httpcore, gitdb, httpx, gitpython, fastapi, gradio-client, streamlit, sentence-transformers, gradio\n",
            "  Attempting uninstall: tenacity\n",
            "    Found existing installation: tenacity 9.0.0\n",
            "    Uninstalling tenacity-9.0.0:\n",
            "      Successfully uninstalled tenacity-9.0.0\n",
            "Successfully installed PyPDF2-3.0.1 aiofiles-23.2.1 faiss-cpu-1.8.0.post1 fastapi-0.115.0 ffmpy-0.4.0 gitdb-4.0.11 gitpython-3.1.43 gradio-4.44.0 gradio-client-1.3.0 h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 orjson-3.10.7 pydeck-0.9.1 pydub-0.25.1 python-multipart-0.0.10 ruff-0.6.6 semantic-version-2.10.0 sentence-transformers-3.1.1 smmap-5.0.1 starlette-0.38.5 streamlit-1.38.0 tenacity-8.5.0 tomlkit-0.12.0 uvicorn-0.30.6 watchdog-4.0.2 websockets-12.0\n"
          ]
        }
      ],
      "source": [
        "!pip install streamlit gradio transformers sentence-transformers faiss-cpu PyPDF2\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wDCtaM53x1Bn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import PyPDF2\n",
        "from sentence_transformers import SentenceTransformer\n",
        "import faiss\n",
        "import numpy as np\n",
        "\n",
        "# Load models\n",
        "embedding_model = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n",
        "# Function to process PDF\n",
        "def process_pdf(pdf_file):\n",
        "    pdf_reader = PyPDF2.PdfReader(pdf_file)\n",
        "    document_text = \"\"\n",
        "    for page in pdf_reader.pages:\n",
        "        document_text += page.extract_text()\n",
        "    sentences = document_text.split('. ')\n",
        "    embeddings = embedding_model.encode(sentences)\n",
        "    faiss_index = faiss.IndexFlatL2(embeddings.shape[1])\n",
        "    faiss_index.add(embeddings)\n",
        "    return sentences, embeddings, faiss_index\n",
        "\n",
        "# Function to get relevant context\n",
        "def get_relevant_context(query, faiss_index, sentences, k=3):\n",
        "    query_vector = embedding_model.encode([query])\n",
        "    _, I = faiss_index.search(query_vector, k)\n",
        "    relevant_sentences = [sentences[i] for i in I[0]]\n",
        "    return \". \".join(relevant_sentences)\n"
      ],
      "metadata": {
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        },
        "id": "wI5GKt_EkffC",
        "outputId": "ce45ee0d-30d8-484b-d70c-83ba4252cd67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "a471b54679db47909ada796f17ee7324"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "qa_model = pipeline(\"question-answering\", model=\"distilbert-base-cased-distilled-squad\")\n",
        "\n",
        "def answer_question(query, faiss_index, sentences):\n",
        "    if not sentences:\n",
        "        return \"Please upload a document first.\", \"\"\n",
        "    relevant_context = get_relevant_context(query, faiss_index, sentences)\n",
        "    answer = qa_model(question=query, context=relevant_context)\n",
        "    return answer['answer'], relevant_context\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
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            "a07be921e2d649969fd3bafa98be159b",
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            "439f54708f3046fd951c23d7b6ae7882",
            "9ac1db52f2f04e479240c0f3b98db22a",
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            "c8027ada694642e3813c309867031fca",
            "3e1f648800954260a806503d75e9d015"
          ]
        },
        "id": "DBGrnQkskiRt",
        "outputId": "18f53777-3e2e-4022-c4b1-b52d2085760a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/473 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/261M [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "1c1b70e2c8f840beabc978024f7ffd03"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "def process_and_answer(pdf_file, query):\n",
        "    sentences, embeddings, faiss_index = process_pdf(pdf_file)\n",
        "    answer, context = answer_question(query, faiss_index, sentences)\n",
        "    return answer, context\n",
        "\n",
        "with gr.Blocks() as demo:\n",
        "    gr.Markdown(\"# Interactive QA Bot\")\n",
        "    pdf_input = gr.File(label=\"Upload PDF\")\n",
        "    query_input = gr.Textbox(label=\"Ask a question about the document\")\n",
        "    answer_output = gr.Textbox(label=\"Answer\")\n",
        "    context_output = gr.Textbox(label=\"Relevant Context\")\n",
        "    submit_button = gr.Button(\"Submit\")\n",
        "\n",
        "    submit_button.click(process_and_answer, inputs=[pdf_input, query_input], outputs=[answer_output, context_output])\n",
        "\n",
        "demo.launch()\n"
      ],
      "metadata": {
        "id": "LhR6DyxPlDCK",
        "outputId": "17b0bb97-993e-4ada-abbe-3e0900cf2574",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://a35335e31a4b6d4cda.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://a35335e31a4b6d4cda.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    }
  ]
}