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https://github.com/codingis4noobs2/QuickDigest
QuickDigest AI facilitates seamless interaction with various data formats, real-time web search, and creative image generation for advertising
https://github.com/codingis4noobs2/QuickDigest
assemblyai hacktoberfest langchain-python llama-index python3 streamlit
Last synced: 3 months ago
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QuickDigest AI facilitates seamless interaction with various data formats, real-time web search, and creative image generation for advertising
- Host: GitHub
- URL: https://github.com/codingis4noobs2/QuickDigest
- Owner: codingis4noobs2
- License: mit
- Created: 2023-09-20T05:51:38.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-29T09:18:57.000Z (9 months ago)
- Last Synced: 2024-04-29T10:34:49.368Z (9 months ago)
- Topics: assemblyai, hacktoberfest, langchain-python, llama-index, python3, streamlit
- Language: Python
- Homepage: https://quickdigest-ai.streamlit.app/
- Size: 114 KB
- Stars: 45
- Watchers: 1
- Forks: 13
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# QuickDigest AI: Elevate Your Data Interaction Experience
Discover a new horizon of data interaction with QuickDigest AI, your intelligent companion in navigating through diverse data formats. QuickDigest AI is meticulously crafted to simplify and enrich your engagement with data, ensuring a seamless flow of insights right at your fingertips.
QuickDigest AI has also won the Streamlit App of the month award, check it out here: https://twitter.com/streamlit/status/1730698585929601085
## Features
### Effortless Data Extraction and Interaction:
QuickDigest AI stands as a beacon of innovation, allowing users to upload and interact with a variety of file formats including PDFs, Word documents, text files, and even audio/video files. The platform's cutting-edge technology ensures a smooth extraction of data, paving the way for meaningful conversations with the information gleaned from these files.### Engage with Datasets:
Dive into datasets like never before. QuickDigest AI invites you to upload your dataset and engage in a dialogue with it. Our advanced AI algorithms facilitate a conversational interaction with your dataset, making the extraction of insights an intuitive and enriching experience.### Real-Time Web Search:
One of the limitations of large language models is there limited knowledge. QuickDigest AI's real-time web search feature ensures you're always ahead with the latest information. Be it market trends, news updates, or the newest research findings, QuickDigest AI brings the world to you in real-time.### Ignite Your Creative Spark:
For product creators, QuickDigest AI unveils a realm of possibilities. Bored of simple product images, The Product Advertising Image Creator is your tool to craft captivating advertising images that resonate with your audience. Additionally, the Image Generator feature is your canvas to bring your creative visions to life, creating visually appealing images that speak volumes.---
### Screenshots:#### Chat with your files
![s1](https://github.com/codingis4noobs2/QuickDigest/assets/87560178/7804d3da-e3e3-49f2-a62e-54ace5c27956)
![s2](https://github.com/codingis4noobs2/QuickDigest/assets/87560178/a644e9cd-f6bc-4d78-8477-caa34569bb5b)#### Chat with your dataset
![s3](https://github.com/codingis4noobs2/QuickDigest/assets/87560178/b441bdf3-a421-4f19-a3e9-f8c7716bfe46)#### Product Theming
![s4](https://github.com/codingis4noobs2/QuickDigest/assets/87560178/19cce56b-dd34-4af1-8590-665856b85778)
![s5](https://github.com/codingis4noobs2/QuickDigest/assets/87560178/7bb69ff8-2d09-40bd-96ac-ba886f5757e3)---
### Getting Started
To get started with this project, clone this repository and install the requirements.
1. Clone the repository:
```
git clone https://github.com/codingis4noobs2/QuickDigest.git
```2. Change to the project directory:
```
cd QuickDigest
```3. Install the required packages:
```
pip install -r requirements.txt
```4. You need to set your own API key
```
assembly_api_key = "YOUR_Aseembly_API_KEY"
clipdrop_api_key = "YOUR_ClipDrop_API_KEY"
```5. Run the app:
```
streamlit run app.py
```6. The app will now be accessible at http://localhost:8501.