Overview This Streamlit app leverages a fine-tuned Gemini AI model to assess the risk level and potential owner of assets based on their descriptions. Users can upload a CSV file containing asset information, and the app will automatically process the data and generate predictions.
Features CSV File Upload: Users can easily upload CSV files containing asset data. Model-Powered Predictions: The app utilizes a fine-tuned Gemini AI model to analyze asset descriptions and determine risk assessments and potential owners. Interactive Visualization: The app presents the results in an interactive dashboard, including tables, bar charts, and pie charts. Customizable Queries: Users can input specific queries to get tailored predictions from the model. How to Use Clone the Repository: Bash git clone https://github.com/EmperorDa8/AI_tunedRisker.git Use code with caution.
Install Dependencies: Bash pip install streamlit pandas plotly google-generativeai Use code with caution.
Set Up API Key: Obtain a Gemini API key from Google AI. Set the API key as an environment variable: Bash export GEMINI_API_KEY=your_api_key Use code with caution.
Run the App: Bash streamlit run your_script.py Use code with caution.
Technical Details Framework: Streamlit AI Model: Fine-tuned Gemini AI model Data Processing: Pandas Visualization: Plotly API Integration: Google Generative AI API Potential Improvements Error Handling: Implement robust error handling to catch exceptions and provide informative feedback to the user. Data Validation: Validate the CSV file format and content to ensure proper processing. Advanced Visualization: Explore more advanced visualization techniques to gain deeper insights into the data. Model Optimization: Fine-tune the model further to improve prediction accuracy and efficiency. User Interface: Enhance the user interface with more intuitive features and a cleaner design. By leveraging the power of AI, this app provides a valuable tool for asset risk assessment and management.