README.md

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

## Video Overview

Watch our comprehensive video demonstration to understand the purpose and usage of the MistyClimate Agent:

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

**Click the image above or [watch the demo video on YouTube](https://youtu.be/b9HGT9l5bcg)** to see:
- How to use the climate chat assistant(Used Agent API)
- ### Agent API
![Agent API](Agent%20API.png)
- Document processing capabilities(OCR Model)
- Image analysis features(Pixtral Model)
- JSON data analysis and speech generation(Large Model)
- Text-to-speech functionality(Large Model)
- Complete workflow demonstrations(Large Model)

## 📷 Preview Images

Below are preview images showcasing key functionalities and model usage:

### Mistral Tokens Usage  
![Mistral Tokens Usage](Mistral%20Tokens%20Usage.png)

### Mistral Agents and OCR Usage  
![Mistral Agents and OCR Usage](Mistral%20Agents%20and%20OCR%20Usage.png)


## Key Features

### Climate Chat Assistant
- **Specialized Climate Intelligence**: Interact with an AI assistant trained on climate science and sustainability topics
- **Expert Guidance**: Get accurate information on climate change, environmental policies, and sustainability practices
- **Interactive Interface**: User-friendly chat interface with emoji avatars and sample questions

### Document Processing
- **Climate Document Analysis**: Extract structured data from climate-related PDFs using OCR capabilities
- **Multi-format Support**: Process various document types including climate reports, analysis papers, and data sheets
- **Structured Output**: Get JSON-formatted results for easy integration

### Image Analysis
- **Visual Data Processing**: Analyze image-based documents (PNG, JPG, PDF) to extract text, charts, and tables
- **Chart Recognition**: Specialized analysis of climate charts and graphs
- **Text Extraction**: OCR capabilities for extracting text from images
- **Table Processing**: Extract structured data from tabular images

### JSON Analysis & Speech
- **Climate Data Analysis**: Analyze JSON data to extract insights and patterns with focus on climate data
- **Audio Generation**: Convert analysis results into speech using advanced TTS
- **Statistical Analysis**: Perform content, statistical, and structural analysis of climate datasets
- **Multi-modal Output**: Get both text analysis and audio summaries

### Text-to-Speech
- **Natural Voice Synthesis**: Convert climate-related text into natural-sounding speech
- **gTTS Integration**: High-quality text-to-speech using Google Text-to-Speech
- **Audio Export**: Generate downloadable audio files



## Technical Architecture

### MCP Server Integration
The MistyClimate Agent utilizes MCP (Model Context Protocol) servers for enhanced functionality:
- **Document Agent MCP Server**: Handles PDF processing and document analysis
- **Image Agent MCP Server**: Manages image analysis and OCR operations
- **Server Link**: [MCP Server Implementation](https://huggingface.co/spaces/Agents-MCP-Hackathon/MistyClimateServer)

### Multi-Agent System
- **Document Agent**: Specialized in climate document processing
- **Image Agent**: Handles visual data analysis
- **JSON Analyzer Agent**: Processes structured climate data
- **Speech Agent**: Manages text-to-speech conversion
- **Climate Chat Agent**: Provides interactive climate intelligence

## Setup and Installation

### Prerequisites

- Docker (for containerized deployment)
- A Mistral API key (obtain from [Mistral AI](https://mistral.ai/))
- Python 3.10+ (for local development)

### Quick Start

1. **Clone the Repository**:
   ```bash
   git clone <repository-url>
   cd <repository-directory>
   ```

2. **Install Dependencies**:
   ```bash
   pip install -r requirements.txt
   ```

3. **Set Up Mistral API Key**:
   - Obtain your API key from [Mistral AI](https://mistral.ai/)
   - Input your API key in the Gradio interface

4. **Run with Docker** (Recommended):
   ```bash
   # Build the Docker image
   docker build -t mistyclimate-agent .
   
   # Run the container
   docker run -p 7860:7860 mistyclimate-agent
   ```

5. **Access the Application**:
   - Open `http://localhost:7860` in your browser
   - Enter your Mistral API key when prompted

### Deployment on Hugging Face Spaces

This project is configured for seamless deployment on Hugging Face Spaces:

1. Fork/clone this repository to your Hugging Face account
2. Create a new Space with `sdk: docker`
3. Push your code to the Space repository
4. The Space will automatically build and deploy

## Usage Guide

### 1. Climate Chat Assistant
- Start by entering your Mistral API key
- Use the chat interface to ask climate-related questions
- Try sample questions provided in the interface
- Get expert guidance on climate science, sustainability, and environmental policies

### 2. Document Processing
- Upload a PDF document (climate reports, research papers)
- Select the document type (climate_report, analysis, data)
- Click "Process Document" to extract structured data
- Review the JSON-formatted output

### 3. Image Analysis
- Upload an image file (PNG, JPG, or PDF)
- Choose analysis focus (text extraction, chart analysis, table extraction)
- Click "Analyze Image" to process the visual data
- Get structured results from the image content

### 4. JSON Analysis & Speech
- Input climate-related JSON data
- Select analysis type (statistical, content, structural)
- Click "Run Analysis & Generate Speech"
- Get both text analysis and audio summary

### 5. Text-to-Speech
- Enter text related to climate topics
- Click "Generate Speech" to create audio
- Download and play the generated audio file

## File Structure

```
├── agent.py              # Core multi-agent system logic
├── app.py                # Gradio interface and workflow orchestration
├── requirements.txt      # Python dependencies
├── Dockerfile           # Docker configuration
├── README.md           # Project documentation
└── ...             # Preview images and media
```

## Configuration

### Environment Variables
- `MISTRAL_API_KEY`: Your Mistral API key (can be set via environment or interface)

### Supported File Formats
- **Documents**: PDF
- **Images**: PNG, JPG, JPEG, PDF
- **Data**: JSON format for climate datasets


## Performance Metrics

- **Document Processing**: Handles climate PDFs up to 50MB
- **Image Analysis**: Supports images up to 10MB
- **Response Time**: Typically 2-5 seconds per request
- **Audio Generation**: High-quality TTS output

## Privacy and Security

- API keys are handled securely and not stored
- All processing is done in real-time without data persistence
- Files are temporarily stored only during processing

## API Reference

For detailed API documentation and integration examples, visit our [MCP Server Implementation](https://huggingface.co/spaces/Agents-MCP-Hackathon/MistyClimateServer).

## Tags

- `agent-demo-track`
- `climate-intelligence`
- `multi-agent-system`
- `mistral-ai`
- `document-processing`
- `image-analysis`
- `text-to-speech`

## License

This project is licensed under the MIT License. See the LICENSE file for details.

## Acknowledgments

- Built with [Mistral AI](https://mistral.ai/) models
- Powered by [Gradio](https://gradio.app/) for the web interface
- Utilizes [Hugging Face Spaces](https://huggingface.co/spaces) for deployment

---

**Built with ❤️ by Samudrala Dinesh Naveen Kumar**

*Making climate intelligence accessible through advanced AI technology*