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https://github.com/saadkh1/docqa-textsummarization-app
A Streamlit app for document question answering and text summarization.
https://github.com/saadkh1/docqa-textsummarization-app
langchain llama-2 llamacpp pytesseract question-answering streamlit summarization whisper
Last synced: about 1 month ago
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A Streamlit app for document question answering and text summarization.
- Host: GitHub
- URL: https://github.com/saadkh1/docqa-textsummarization-app
- Owner: saadkh1
- License: apache-2.0
- Created: 2023-08-31T22:42:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-01T06:14:02.000Z (over 1 year ago)
- Last Synced: 2023-11-17T23:58:52.589Z (about 1 year ago)
- Topics: langchain, llama-2, llamacpp, pytesseract, question-answering, streamlit, summarization, whisper
- Language: Jupyter Notebook
- Homepage:
- Size: 190 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Streamlit Document QA and Text Summarization App
This Streamlit application empowers users to effortlessly perform Document Question-Answering (QA) and Text Summarization tasks in their preferred language, English or German, with just a few simple steps.
## How to Use
### Language Selection
1. Select your preferred language (English or German) from the sidebar.
### Task Selection
2. Choose the task you want to perform - Document QA or Text Summarization.
### Document QA
#### 1. Upload Documents
- Click on the "Upload Files" section in the sidebar.
- Upload various types of documents including (PDFs, Markdown, plain text, and DOCX files).
#### 2. OCR for Images
- Click on the "OCR for Images" section in the sidebar.
- Conveniently upload images (PNG, JPG, JPEG) for optical character recognition (OCR).
#### 3. Upload Audio Files and Transcribe
- Click on the "Upload Audio Files and Transcribe" section in the sidebar.
- Effortlessly upload audio files (MP3, WAV) for automatic transcription.
#### 4. Import HTML
- Click on the "Import HTML" section in the sidebar.
- Simply enter URLs to import HTML content from websites.
#### 5. Transcribe YouTube Video
- Click on the "YouTube Video" section in the sidebar.
- Enter a YouTube video URL for transcription.
#### 6. Create Vector Database
- Click on the "Create Vector Database" section in the sidebar to create a database from uploaded documents.
#### 7. Remove All Files
- Click on the "Remove Files" section in the sidebar to remove all files in the data directory.
#### Chat with Chatbot
- Engage with a chatbot that can provide answers to questions based on the uploaded documents.
### Text Summarization
- In the Text Summarization task, simply enter text in the provided text area.
- Click the "Summarize" button to generate a concise summarization of the input text.
### Document QA Example:
Here's an example of the Document Question-Answering task in action:
### Text Summarization Example:
And here's an example of the Text Summarization task in action:
## Installation and Running Locally
To use this Streamlit application, follow these steps:
1. **Clone the repository and navigate to the project directory:**
```bash
git clone https://github.com/saadkh1/DocQA-TextSummarization-App.git
```
```bash
cd DocQA-TextSummarization-App
```
2. **Install the required packages from the requirements.txt file:**
```bash
pip install -r requirements.txt
```
3. **Download the necessary language models and embeddings by running the models.sh script:**
```bash
sh models.sh
```
4. **Run the Streamlit app:**
```bash
streamlit run app.py
```
5. **Open this URL in your browser:** http://localhost:8501/
## Using Docker
Alternatively, you can use Docker to run the application in a container. Make sure you have Docker installed on your system. Follow these steps:
1. **Clone the repository and navigate to the project directory:**
```bash
git clone https://github.com/saadkh1/DocQA-TextSummarization-App.git
```
```bash
cd DocQA-TextSummarization-App
```
2. **Build the Docker image:**
```bash
docker build -t qa-summrize-app:1.0 .
```
3. **Run the Docker container:**
```bash
docker run -p 8501:8501 qa-summrize-app:1.0
```
4. **Open this URL in your browser:** http://localhost:8501/
## Using Google Colab
If you prefer to use Google Colab, you can run the app using the provided app.ipynb notebook:
1. **Open the app.ipynb notebook in Google Colab:**
2. **Run all the cells in the notebook.**
The notebook will start the Streamlit app and expose it using ngrok. Follow the instructions in the notebook to access the app URL.