Update README.md

README.md

@@ -6,8 +6,93 @@ colorTo: red
 sdk: streamlit
 sdk_version: 1.36.0
 app_file: app.py
-pinned: false
+pinned: true
 license: mit
+short_description: Upload a PDF and ask question about it
 ---
+# RAG-based PDF Query System
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This project implements a Retrieval-Augmented Generation (RAG) system that allows users to upload multiple PDF files, extract and preprocess the text, and then query the contents of those PDFs using OpenAI's GPT-3.5-turbo model. The system combines the strengths of information retrieval and text generation to provide accurate and context-aware responses to user queries.
+
+## Description
+
+The RAG-based PDF Query System is designed to:
+1. **Extract Text from PDFs:** Utilize `pdfplumber` to accurately extract text from multiple PDF files.
+2. **Preprocess Text:** Clean and tokenize the extracted text for better processing.
+3. **Create a Knowledge Base:** Use TF-IDF vectorization to create a searchable knowledge base from the extracted text.
+4. **Retrieve Relevant Texts:** Retrieve the most relevant texts based on the user query using cosine similarity.
+5. **Generate Responses:** Use OpenAI's GPT-3.5-turbo model to generate responses based on the retrieved texts and user query.
+
+### Key Components and Technologies Used
+
+- **Streamlit:** For building an interactive web application.
+- **pdfplumber:** For extracting text from PDF files.
+- **NLTK:** For text preprocessing tasks such as tokenization.
+- **Scikit-learn:** For TF-IDF vectorization and text retrieval.
+- **OpenAI GPT-3.5-turbo:** For generating context-aware responses to user queries.
+
+### Why This Project?
+
+- **Combining Retrieval and Generation:** The project combines information retrieval with advanced text generation, providing users with accurate and context-aware responses.
+- **Interactive Interface:** Streamlit offers an easy-to-use interface for uploading PDFs and querying their contents.
+- **Advanced Text Extraction:** `pdfplumber` ensures accurate extraction of text from PDFs, even from complex layouts.
+- **State-of-the-art Language Model:** OpenAI's GPT-3.5-turbo is one of the most advanced language models, ensuring high-quality responses.
+
+## How to Run
+
+### Prerequisites
+
+- Python 3.7 or higher
+- OpenAI API Key (you can get it from the [OpenAI website](https://beta.openai.com/signup/))
+
+### Installation
+
+1. **Clone the repository:**
+    ```bash
+    git clone https://github.com/your-username/rag-pdf-query-system.git
+    cd rag-pdf-query-system
+    ```
+
+2. **Create a virtual environment and activate it:**
+    ```bash
+    python -m venv env
+    source env/bin/activate  # On Windows use `env\Scripts\activate`
+    ```
+
+3. **Install the required packages:**
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+4. **Download NLTK data:**
+    ```python
+    import nltk
+    nltk.download('punkt')
+    ```
+
+5. **Create a `.env` file in the project root directory:**
+    ```text
+    OPENAI_API_KEY=your_openai_api_key_here
+    ```
+
+### Running the Application
+
+1. **Run the Streamlit application:**
+    ```bash
+    streamlit run app.py
+    ```
+
+2. **Use the Application:**
+    - Open the URL provided by Streamlit (usually `http://localhost:8501`) in your web browser.
+    - Upload one or more PDF files.
+    - Enter your query in the input box.
+    - View the generated response based on the contents of the uploaded PDFs.
+
+### Notes
+
+- The progress bar in the Streamlit application provides real-time feedback during the PDF processing stages.
+- Ensure you have a stable internet connection to interact with the OpenAI API for generating responses.
+
+This project demonstrates the integration of various tools and libraries to create a powerful and interactive query system for PDF documents.
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference