PLANA: Plan Learning, Analysis, and Advisory

This repository contains a Python script for analyzing various aspects of a business plan, including executive summary, market analysis, financial projections, and additional documentation. The script leverages a combination of machine learning models and OpenAI's GPT-3 to generate a comprehensive grade and actionable feedback for the business.

Features

• Executive Summary Analysis: Uses BERT-based sentiment analysis to evaluate the sentiment of the executive summary, mission, and vision statement.
• Market Analysis: Analyzes market sentiment, segments the market, and forecasts market trends using text analysis and clustering.
• Financial Projections: Forecasts financial metrics and performs ratio analysis and anomaly detection.
• Credit Risk and Fraud Detection: Assesses credit risk and detects potential fraud in financial data.
• Document Classification and Named Entity Recognition: Classifies additional documentation and extracts named entities.

Requirements

You can install all the required packages using the requirements.txt file included in this repository.

Architecture Overview

The application consists of:

• Frontend: User interface for uploading business plans and viewing results
• Backend: Flask server for processing and analysis
• Database: PostgreSQL for storing data
• Machine Learning Models: Various models for analysis tasks

Setup and Running the Application

Prerequisites

• Python 3.8 or later
• Docker and Docker Compose

Step 1: Set Up the Environment

1. Create a virtual environment and install dependencies.

   # Create a virtual environment
   python -m venv venv
   
   # Activate the virtual environment
   # On Windows
   venv\Scripts\activate
   # On Unix or MacOS
   source venv/bin/activate
   
   # Install dependencies
   pip install -r requirements.txt

Step 2: Create the Database

2. Ensure the database schema is created.

   # Create the database
   python

   In the Python shell, run:

   from app import db
   db.create_all()
   exit()

Step 3: Run the Flask Application

3. Run the Flask application.

   # Set the FLASK_APP environment variable
   export FLASK_APP=app.py
   # On Windows use `set` instead of `export`
   set FLASK_APP=app.py
   
   # Run the Flask development server
   flask run

The application will be available at http://localhost:5000.

Running the Application using Docker Compose

To run the application using Docker Compose, follow these steps:

Step 1: Build and Run the Docker Containers

1. Ensure your Dockerfile and docker-compose.yml are correctly set up.

Dockerfile

# Use an official Python runtime as a parent image
FROM python:3.8-slim

# Set the working directory in the container
WORKDIR /app

# Copy the current directory contents into the container at /app
COPY . /app

# Install any needed packages specified in requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Make port 5000 available to the world outside this container
EXPOSE 5000

# Define environment variable
ENV FLASK_APP=app.py

# Run app.py when the container launches
CMD ["flask", "run", "--host=0.0.0.0"]