---
title: MistyClimate Agent
emoji: 📈
colorFrom: red
colorTo: pink
sdk: docker
pinned: false
short_description: This is a agent created using mistral models
tags:
  - agent-demo-track
Usage: Mistral
---
# MistyClimate Agent 📈

This is an agent created using Mistral models, designed to process climate-related documents, analyze images, perform JSON data analysis, and convert text to speech. It provides a multi-agent system for document processing, image analysis, JSON analysis, and text-to-speech functionalities, all integrated into a user-friendly Gradio interface.

## Video Demo
Below is an embedded YouTube video demonstrating the MistyClimateAgent for the Hackathon:

[![Watch the Demo Video](https://img.youtube.com/vi/b9HGT9l5bcg/0.jpg)](https://youtu.be/b9HGT9l5bcg)

Click the image or [watch the demo video on YouTube](https://youtu.be/b9HGT9l5bcg).

## Features

- **Document Processing**: Extract structured data from climate-related PDFs using OCR capabilities.
- **Image Analysis**: Analyze image-based documents (e.g., PNG, JPG, PDF) to extract text, charts, and tables.
- **JSON Analysis**: Analyze JSON data to extract insights and patterns, with a focus on climate data.
- **Text-to-Speech**: Convert text analysis into speech using the gTTS library.
- **Gradio Interface**: A web-based UI to interact with all features seamlessly.

## Setup

This project is containerized using Docker and deployed on a Gradio Space. Follow the steps below to set up and run the project locally or on Hugging Face Spaces.

### Prerequisites

- Docker (if running locally)
- A Mistral API key (obtain from [Mistral AI](https://mistral.ai/))
- Python 3.10+ (if running locally without Docker)

### Installation

1. **Clone the Repository** (if running locally):
   ```bash
   git clone <repository-url>
   cd <repository-directory>
   ```

2. **Install Dependencies**:
   The project uses a `requirements.txt` file to manage dependencies. If running locally without Docker, install the dependencies using:
   ```bash
   pip install -r requirements.txt
   ```

3. **Set Up the Mistral API Key**:
   - You will need a Mistral API key to use the Mistral models.
   - In the Gradio interface, input your API key in the "Mistral API Key" field.

4. **Run with Docker** (recommended for local testing):
   - Build the Docker image:
     ```bash
     docker build -t mistyclimate-agent .
     ```
   - Run the Docker container:
     ```bash
     docker run -p 7860:7860 mistyclimate-agent
     ```
   - Access the Gradio interface at `http://localhost:7860`.

5. **Deploy on Hugging Face Spaces**:
   - This project is already configured for Hugging Face Spaces with the `sdk: docker` setting.
   - Push your code to a Hugging Face Space repository.
   - The Space will automatically build and deploy using the provided `Dockerfile`.

## Usage

1. **Access the Gradio Interface**:
   - If running locally, open `http://localhost:7860` in your browser.
   - If deployed on Hugging Face Spaces, visit the Space URL.

2. **Enter Your Mistral API Key**:
   - In the Gradio interface, provide your Mistral API key in the designated input field.

3. **Interact with the Tabs**:
   - **Document Processing**:
     - Upload a PDF document (e.g., a climate report).
     - Select the document type (e.g., `climate_report`).
     - Click "Process Document" to extract structured data in JSON format.
   - **Image Analysis**:
     - Upload an image file (PNG, JPG, or PDF).
     - Choose an analysis focus (e.g., `text_extraction`, `chart_analysis`).
     - Click "Analyze Image" to get structured data from the image.
   - **JSON Analysis & Speech**:
     - Input JSON data (e.g., temperature or emissions data).
     - Select an analysis type (e.g., `content`).
     - Click "Run Analysis & Speech" to analyze the JSON and generate a speech output.
   - **Text-to-Speech**:
     - Enter text to convert to speech (e.g., "hello, and good luck for the hackathon").
     - Click "Generate Speech" to produce and play an audio file.

## File Structure

- `agent.py`: Core logic for the multi-agent system, including document processing, image analysis, JSON analysis, and text-to-speech workflows.
- `app.py`: Gradio interface setup and workflow orchestration.
- `requirements.txt`: List of Python dependencies.
- `Dockerfile`: Docker configuration for containerizing the app.
- `README.md`: Project documentation (this file).

## Notes

- **File Paths**: In a Gradio Space, files like PDFs, images, and WAVs are handled dynamically via uploads. Output files (e.g., WAVs) are saved to `/tmp/` during runtime.
- **Mistral API Key**: Ensure you have a valid Mistral API key to use the models. Without it, the workflows will fail.
- **Docker Deployment**: The project is configured to run in a Docker container, making it compatible with Hugging Face Spaces.

## Configuration Reference

For more details on configuring Hugging Face Spaces, refer to the [Hugging Face Spaces Config Reference](https://huggingface.co/docs/hub/spaces-config-reference).

## Tags

- `agent-demo-track`

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details (if applicable).

---

Built with ❤️ by  Samudrala Dinesh Naveen Kumar.