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.

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.