Upload Documentation.ipynb

Documentation.ipynb

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+{
+ "cells": [
+  {
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+   "metadata": {},
+   "source": [
+    "The code you've provided is a Python application that utilizes multiple libraries to create a Gradio interface. The main functionality of this app is to allow users to upload PDF documents, extract text, and either summarize the document or answer questions based on the content using a language model (GPT-4). Here's a step-by-step breakdown of the code:\n",
+    "\n",
+    "### 1. **Imports**\n",
+    "   - **openai**: This is used to interact with the OpenAI API. It's required to access GPT models (like GPT-4).\n",
+    "   - **gradio**: This library is used to create the user interface (UI) for the app. It allows you to build simple web interfaces.\n",
+    "   - **langchain.chains**: This library is used for creating chains that allow interaction with large language models (LLMs) like GPT-4. In this case, it’s used for querying and summarization of documents.\n",
+    "   - **langchain.llms**: This is used to define the language models, in this case, GPT-4.\n",
+    "   - **langchain.document_loaders**: This library is used to load PDF documents.\n",
+    "   - **langchain.embeddings.openai**: This library is used to create embeddings from the PDF document’s content. Embeddings help represent the document's information in a format that can be understood by the language model.\n",
+    "   - **langchain.vectorstores**: This library stores the embeddings (representations of the document's content) in a vector store (FAISS in this case).\n",
+    "   - **langchain.chat_models**: This is used to interact with chat-based language models like GPT-4.\n",
+    "   - **PyPDF2**: This is another library to process PDF files (though it's not actively used in this code, it's imported).\n",
+    "\n",
+    "### 2. **Functions Breakdown**\n",
+    "\n",
+    "#### **`load_pdf(file)`**\n",
+    "This function is responsible for loading the content of a PDF document. It uses LangChain's `PyPDFLoader` to extract text from the PDF file.\n",
+    "- **Input**: A PDF file.\n",
+    "- **Output**: A list of documents (text extracted from the PDF).\n",
+    "\n",
+    "#### **`summarize_pdf(file, openai_api_key)`**\n",
+    "This function is designed to summarize the content of the uploaded PDF document using GPT-4.\n",
+    "- **Input**: The uploaded PDF file and the OpenAI API key.\n",
+    "- **Process**:\n",
+    "  - The PDF is processed to extract text using `load_pdf()`.\n",
+    "  - Embeddings are generated for the extracted text using the OpenAI API.\n",
+    "  - The `FAISS` vector store is used to store these embeddings and make them searchable.\n",
+    "  - A `RetrievalQA` chain is created, which will use the stored embeddings to provide answers or summaries based on the PDF.\n",
+    "  - The `qa_chain.run(\"Summarize the content of the research paper.\")` command asks the model to summarize the document.\n",
+    "- **Output**: The summary of the document generated by GPT-4.\n",
+    "\n",
+    "#### **`query_pdf(file, user_query, openai_api_key)`**\n",
+    "This function allows users to ask questions based on the content of the uploaded PDF.\n",
+    "- **Input**: The uploaded PDF file, a user query (the question), and the OpenAI API key.\n",
+    "- **Process**:\n",
+    "  - The PDF is processed and embeddings are created as in the `summarize_pdf` function.\n",
+    "  - The `FAISS` vector store is created with the embeddings.\n",
+    "  - The `RetrievalQA` chain is used to query the document using the user’s question.\n",
+    "  - The model will retrieve relevant information from the PDF and provide an answer.\n",
+    "- **Output**: The answer to the user's query, based on the PDF content.\n",
+    "\n",
+    "### 3. **Creating the Gradio Interface**\n",
+    "\n",
+    "The `create_gradio_interface()` function creates the web interface for the application. Gradio is used to create a simple user-friendly web interface where users can interact with the application.\n",
+    "\n",
+    "#### **Summary Tab (Summarize PDF)**\n",
+    "\n",
+    "- **Inputs**:\n",
+    "  - **`openai_api_key_input`**: A password-protected textbox where the user enters their OpenAI API key.\n",
+    "  - **`pdf_file`**: A file upload button for users to upload a PDF document.\n",
+    "  - **`summarize_btn`**: A button that triggers the summarization process.\n",
+    "- **Outputs**:\n",
+    "  - **`summary_output`**: A textbox that displays the summarized content of the PDF after it's processed.\n",
+    "  - **`clear_btn_summary`**: A button that clears the summary from the output textbox.\n",
+    "  \n",
+    "When the \"Summarize\" button is clicked, it calls the `summarize_pdf` function, passing in the PDF file and the API key. The result (summary) is displayed in the `summary_output` textbox.\n",
+    "\n",
+    "#### **Ask Questions Tab (Query PDF)**\n",
+    "\n",
+    "- **Inputs**:\n",
+    "  - **`pdf_file_q`**: A file upload button for users to upload a PDF document (same as the Summarize tab).\n",
+    "  - **`user_input`**: A textbox where the user enters a question related to the content of the PDF.\n",
+    "  - **`openai_api_key_input`**: The OpenAI API key textbox.\n",
+    "- **Outputs**:\n",
+    "  - **`answer_output`**: A textbox that shows the answer to the user's query after it's processed.\n",
+    "  - **`clear_btn_answer`**: A button that clears the answer from the output textbox.\n",
+    "  \n",
+    "When the user types a question and submits it (by pressing enter), it calls the `query_pdf` function, passing in the PDF file, the user's question, and the API key. The answer is displayed in the `answer_output` textbox.\n",
+    "\n",
+    "### 4. **Running the Gradio App**\n",
+    "\n",
+    "At the end of the script:\n",
+    "- The `create_gradio_interface()` function is called to build the interface.\n",
+    "- `demo.launch(debug=True)` starts the Gradio app, making the interface accessible in a web browser. The `debug=True` option ensures that any issues are displayed in the console while running.\n",
+    "\n",
+    "### Key Features:\n",
+    "- **Upload a PDF**: Users can upload a PDF document.\n",
+    "- **Summarization**: Users can get a summary of the document.\n",
+    "- **Ask Questions**: Users can ask questions about the document, and the model will respond with answers based on the content.\n",
+    "- **OpenAI API Key**: The app uses the user's OpenAI API key for interaction with GPT-4.\n",
+    "  \n",
+    "### Conclusion\n",
+    "In short, this app allows users to upload PDF documents and use GPT-4 to either summarize the document or answer specific questions related to the content. The app uses Gradio for the UI, LangChain for document processing, and FAISS for efficient storage and search of document embeddings."
+   ]
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+   "name": "python"
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