AgroTech Solutions is a comprehensive platform designed to mitigate excess crop sowing and raw material storage losses through timely crop predictions, buyer recommendations, and customized advice for farmers and dealers. The system employs advanced technologies, including AI/ML-driven recommendations, user validation through government-issued documents, GPS-based information, and timely alerts for effective inventory management.
- Leverages machine learning algorithms for timely crop predictions for farmers.
- Provides buyer recommendations for dealers based on market trends and historical data.
- Customizes advice for users, reducing raw material waste and recommending crops in their preferred language.
- Implements a secure user validation process using government-issued documents, ensuring the authenticity of users.
- Tailors recommendations to specific user roles, utilizing advanced AI/ML models for personalized advice.
- Offers GPS-based information, guiding users to the nearest Agriculture Produce Market Committee (APMC) for convenient transactions.
- Sends timely alerts to manufacturers regarding product expiration, facilitating optimal inventory management.
- Features an algorithm for brokers, recommending industries based on historical requirements to minimize storage losses.
- Python (Programming Language): The core programming language used for backend development.
- Pyaadhar: Utilized for user validation through government-issued documents.
- Machine Learning: Employed for crop predictions and AI/ML-driven recommendations.
- Flask: Used for building the web application and API.
- OpenCV: Applied for image processing tasks.
- Amazon Web Services (AWS): Hosts the platform, ensuring scalability and reliability.
- Python (Programming Language)
- Pyaadhar
- Machine Learning
- Flask
- Application Programming Interfaces (API)
- OpenCV
- Amazon Web Services (AWS)
Certainly! Below are the installation and running steps for the chatbot using Ngrok and Flask server based on the provided content:
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Python 3.x: Ensure that you have Python 3.x installed on your system.
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Ngrok: Install Ngrok for exposing the local Flask server to the internet. You can download it from https://ngrok.com/.
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Flask and Required Dependencies: Install the necessary Python packages using the following command:
pip install Flask
git clone https://github.com/sarthak4399/krishimitra-dev.git
cd krishimitra-devOpen the chatbot_app.py file and update any configuration parameters such as API keys or other settings if needed.
python chatbot_app.pyThis will start the Flask server locally.
Open a new terminal window and navigate to the directory where Ngrok is located. Run the following command to expose the local Flask server:
./ngrok http 5000Ngrok will provide a public URL (e.g., https://your-ngrok-subdomain.ngrok.io). Note this URL for the next step.
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Create a Twilio account if you don't have one: https://www.twilio.com/try-twilio.
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Set up a Twilio WhatsApp Sandbox: https://www.twilio.com/docs/sms/whatsapp/sandbox.
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Update the WhatsApp Sandbox with the Ngrok URL obtained in the previous step.
Now, your chatbot is ready to receive and respond to messages on WhatsApp. Start a conversation with your Twilio WhatsApp number and interact with the chatbot.
This project is licensed under the MIT License - see the LICENSE file for details.
- Special thanks to the open-source communities and contributors in Python, Flask, Ngrok, Twilio and machine learning for their valuable tools and libraries.
- Thanks to the AWS team for providing reliable cloud services to host this innovative solution.
Feel free to contribute to this project by submitting issues or pull requests. Your feedback and enhancements are highly appreciated!