This app is purely for cricket enthusiats, technologists who love to integrate tech into the domain of cricket and sports.
This app helps to provide key insights about the match.
Hence utilise the freedom of prompt/intention and individual with approporiate terms,usage of language.
The Cricket Predictor App utilizes the Gemini Pro pre-trained model to predict cricket match outcomes based on various input factors such as team names, stadium conditions, pitch types, and more.
This app provides cricket enthusiasts and analysts with accurate match insights, enhancing their understanding and enjoyment of the game.
The primary objective of the Cricket Predictor App is to provide users with precise and insightful predictions of cricket match outcomes.
By leveraging advanced AI models and considering various factors that influence the game, the app aims to assist cricket fans, analysts, and enthusiasts in making informed predictions and analyses.
To achieve the objective, the app integrates the Gemini Pro pre-trained model through a streamlined process:
User Interaction: Users interact with an intuitive UI to enter match details such as team names, stadium, pitch conditions, and other relevant factors.
Data Collection: The app securely collects user inputs and transmits them to the backend using a Google API key.
API Integration: User inputs are forwarded to the Gemini Pro model via an API call.
Model Processing: The Gemini Pro pre-trained model processes the inputs and generates predictions.
Result Display: The results are returned to the frontend, where they are formatted and displayed to the user.
User Input Collection: Intuitive interface for entering match details.
Prediction Generation: Uses the Gemini Pro model to generate detailed match predictions.
Secure Data Handling: Ensures data privacy and security.
Customizable Settings: Allows configuration of prediction parameters.
Performance Optimization: Delivers rapid and reliable predictions.
Deployment Ready: Scalable and accessible from any device.
Sample 1
Public Link: https://ai-ipl-win-predictor-assistant-webapp-dgd9dapr4jga9wrryjpkxg.streamlit.app/
Python 3.7+
Streamlit
Google Generative AI library
dotenv
Software Requirements:
VsCode
AI Studio Account
Streamlit Community Account for Deployment
To clone the repository, use the following command:
git clone (https://github.com/Aniruddhan15/AI-IPL-Win-Predictor-Assistant-WebApp.git)
cd ai-Cricket-Assisstant-app
pip install -r requirements.txt
Obtain an API key from makersuite google for gemini pro vision api key:
Visit the AI Studio website (https://aistudio.google.com/app/apikey) and sign up in order to obtain the API key access.
Follow the instructions provided by AI studio Google to obtain an API key.
Copy the API key as you will need it in the next step.
Add your API key to the app.py file:
Open the Cricket.py file in a text editor.
Locate the line that says genai.api_key = 'YOUR_API_KEY'.
Replace 'YOUR_API_KEY' with the API key you obtained from AIStudio.
Save the Cricket.py file.
Create a ".env" file to your environment and add your api key as google_api_key="(Put Your api key, please dont forget to include as a string)"
use the below code:
from dotenv import load_dotenv
load_dotenv()
genai.configure(api_key=os.getenv("google_api_key"))
python Cricket.py
To start Streamlit web browser, use the following command:
streamlit run Cricket.py
You can view your web browser at (http://localhost:5000) (or http://127.0.0.1:5000) or at a recommended browser link in the comman prompt console.
Register your desired meal picture and a prompt about it start receiving personalized meal plans and tracking your nutrition.
We welcome contributions to enhance the AI Nutrition App.
please fork the repository, create a new branch for your feature or bug fix, and submit a pull request.
Make sure to follow our coding guidelines and include appropriate tests.
Fork the repository.Create a new branch.
git checkout -b feature-branch
Make your changes.
Commit your changes.
git commit -am 'Add new feature'
Push to the branch
git push origin feature-branch
Create a new Pull Request.
Choose a hosting platform (e.g., Heroku, AWS, Google Cloud Platform) and deploy your application following platform-specific instructions.
I used Streamlit Deploy and hosting services
Apache License
Version 2.0, January 2004
N Aniruddhan - aniruddhan26@gmail.com