Streamlit App: Crisis Forecasting Tool

This project is part of ACAPS initiative to build a custom workflow leveraging AI-powered tools for real-time crisis forecasting.

Table of Contents

1. Features
2. Setup and Installation
3. Configuration
4. Usage
5. Additional Notes

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Features

• AI-driven sentiment analysis using Twitter, Google Trends, and news data.
• Supports over 20 languages with voice responses.
• Integration with ACLED data for enhanced analysis.
• Data visualization and insights for decision-making.

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Setup and Installation

Follow the steps below to set up the application:

1. Clone the Repository

git clone https://github.com/your-repo-name/your-project-name.git
cd your-project-name

2. Create a Virtual Environment

python -m venv venv

3. Activate the Virtual Environment

Windows:

venv\Scripts\activate

macOS/Linux:

source venv/bin/activate

4. Install Dependencies

pip install -r requirements.txt

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Configuration

Create a secrets.toml file in the .streamlit folder with the following content:

APIFY_TOKEN = "apify_api_???"
MODEL_KEY = "hf_???"
OPENAI_API_KEY = "sk-proj-???"
SERPER_API_KEY = "???"
HUGGING_FACE_API_KEY = "hf_???"
GROQ_API_KEY = "gsk_???"
APIFY_TWITTER_TOKEN = "apify_api_???"
ELEVEN_API_KEY = ""
ACLED_API_KEY = "???"
ACLED_EMAIL = "???@???.???"

[acaps]
username = "???@???.???"
password = "???@???.???"

Replace the placeholders (???) with your actual API keys and credentials.

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Usage

1. Run the Application

streamlit run app.py

2. Access the Application

Open your browser and navigate to the URL provided by Streamlit (e.g., http://localhost:8501).

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Additional Notes

• Ensure all dependencies are installed and API keys are correctly configured.
• Use the provided CLI commands to set up the environment quickly.
• For troubleshooting, refer to the Streamlit documentation or raise an issue in the repository.