Building Bonds: The Power of Ice-Breakers

Welcome to Building Bonds, a Streamlit application that harnesses the strengths of Amazon Bedrock and LangChain. Make your introductions more memorable! Enter a name, and let our application search for their LinkedIn profile, then provide you with a concise summary and ice-breaking facts about that person.

Features

Instant LinkedIn Search: Just provide a name, and the application will try to locate their LinkedIn profile from the internet.
Automated Summary: With the capabilities of Amazon Bedrock and LangChain, receive a detailed overview of the person's career and accomplishments.
Ice-Breaker Facts: Start your conversation with a bang! Learn unique and engaging facts related to the individual.

How It Works

The magic behind Building Bonds:

Amazon Bedrock: Empowers our system to deep dive into data and bring out meaningful insights.
LangChain: Assists with linguistic processing, allowing the app to draw a clear and engaging summary from LinkedIn details.

Getting Started

1. Pre-requisites

Clone the repository to your local machine.
Create a .env file in the project directory using env.example as a reference. Populate the .env file with your Proxycurl and Serpa API Key details:
```
PROXYCURL_API_KEY=<YOUR API KEY>
SERPAPI_API_KEY=<YOUR API KEY>
```

2. Setting Up a Virtual Environment

Use virtualenv to create an isolated Python environment:

Install virtualenv:
```
pip install virtualenv
```
Navigate to the directory where you cloned the repository.
Initialize the virtual environment:
```
virtualenv bb-env
```
Activate the environment:
```
source bb-env/bin/activate 
```

3. Installing Dependencies

With your virtual environment active, install the necessary packages:

pip install -r requirements.txt

This command installs all dependencies from the requirements.txt file into your rs-env environment.

4. Usage

Launch the application using Streamlit:

streamlit run app.py