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Aspect Ratio Estimation of a Two-Stage Operational Amplifier

This repository contains a Streamlit web application that estimates the aspect ratios of a two-stage operational amplifier using various machine learning models. The application allows users to input specific parameters and select a model to predict the aspect ratios.

Features

  • Interactive UI: User-friendly interface to input parameters and select models.
  • Multiple Models: Provides predictions using different regression models including Linear Regression, Gaussian Process Regression, SVR, Decision Tree, KNN, Random Forest, and a Neural Network.
  • Visualization: Displays predictions and aspect ratios for the selected model.

Screenshots

drawing    drawing   

Getting Started

Prerequisites

  • Python 3.x
  • Streamlit
  • Keras
  • Scikit-learn
  • Numpy
  • Pandas
  • Matplotlib

Installation

  1. Clone the repository:

    git clone https://github.com/Aftaab25/2-Stage-OpAmp-Analysis.git
cd 2-Stage-OpAmp-Analysis

  2. Install the required packages:

    pip install -r requirements.txt

  3. Ensure you have the dataset 2STAGEOPAMP_DATASET.csv in the same directory.

  4. Ensure you have the trained models model.h5 and gaussian_model.pkl in the same directory.

Running the App

Run the Streamlit app using the following command:

streamlit run main.py

This will start the Streamlit server, and you can interact with the app in your web browser.

Usage

  1. Input Features:

    • DC Gain
    • Unity Gain Frequency (ft)
    • 3-dB Frequency (f3)
    • Common Mode Voltage (Vcm)
    • Power Dissipation (Pdiss)

  2. Select a Model:

    • Linear Regression Model
    • Gaussian Regression Model
    • SVR
    • Decision Tree Regressor
    • KNN
    • Random Forest Regressor
    • Neural Network (Best)

  3. Get Predictions: Click the 'Calculate' button to get the predicted aspect ratios for the given input features.

Code Overview

main.py

  • Imports: Necessary libraries including Streamlit, Numpy, Pandas, Scikit-learn, and Keras.
  • Data Loading: Loads the dataset 2STAGEOPAMP_DATASET.csv and preprocesses it.
  • Model Loading: Loads the pre-trained models for prediction.
  • Model Functions: Defines functions for each machine learning model to predict aspect ratios.
  • Streamlit UI: Creates the sidebar and main panel for user input and model selection.

License

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