Pour_Decisions/road_map at main · samannetts8/Pour_Decisions · GitHub

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1. Project Overview
Goal:

    Users upload a photo of a wine label.
    The app scans the label, extracts key details (e.g., wine name, vintage), and retrieves scores or related information from Vivino and Spoonacular.

Key Features:

    Upload photo (frontend).
    Process and analyze the image (backend).
    Retrieve and display data (API integration and UI).

2. Roadmap by Stages
Stage 1: Plan the Architecture

    Goal: Define the app's components and technologies.
    Tasks:
        Break the app into frontend, backend, and database components.
        Decide on libraries and frameworks (Flask for Python backend, React.js for frontend).

Tools and Skills Needed:

    Languages: Basic Python, HTML/CSS.
    Timeline: 1–2 weeks.
    Learning Focus:
        Python basics (syntax, functions, file handling).
        HTML/CSS for structuring and styling web pages.

Stage 2: Build the Frontend (UI)

    Goal: Create a simple webpage where users can upload photos.
    Tasks:
        Build a form to accept image uploads.
        Display the results (wine details and scores).

Tools and Skills Needed:

    Languages: HTML/CSS, JavaScript.
    Framework: React.js (optional, for interactivity).
    Timeline: 3–4 weeks.
    Learning Focus:
        Basic JavaScript for handling form submissions and DOM manipulation.
        React.js if you want modern frontend development experience.

Stage 3: Develop the Backend

    Goal: Set up a Python backend to handle image uploads and perform tasks.
    Tasks:
        Create a Flask app to process uploaded images.
        Save images temporarily for processing.

Tools and Skills Needed:

    Languages: Python.
    Framework: Flask.
    Timeline: 2–3 weeks.
    Learning Focus:
        Flask basics: routes, file handling, and connecting with the frontend.
        Use Python libraries like Flask-Upload for file handling.

Stage 4: Implement Image Processing

    Goal: Extract key details (e.g., wine name, vintage) from the uploaded image.
    Tasks:
        Use Optical Character Recognition (OCR) to scan text from the image.
        Extract and parse relevant details.

Tools and Skills Needed:

    Languages: Python.
    Libraries:
        Tesseract OCR: For text extraction from images.
        OpenCV: For image preprocessing (e.g., resizing, filtering).
    Timeline: 4–5 weeks.
    Learning Focus:
        How to install and use Tesseract and OpenCV.
        Basics of image processing and text extraction.

Stage 5: API Integration for Scores

    Goal: Fetch wine scores and details from Vivino and Spoonacular using APIs.
    Tasks:
        Query APIs using extracted details.
        Handle API responses (e.g., JSON) and pass data to the frontend.

Tools and Skills Needed:

    Languages: Python for API calls, JavaScript for displaying data dynamically.
    Libraries:
        Python’s requests library for API communication.
        JavaScript’s fetch for real-time updates on the frontend.
    Timeline: 3–4 weeks.
    Learning Focus:
        Basics of REST APIs (GET, POST).
        JSON parsing in Python and JavaScript.

Stage 6: Database Integration

    Goal: Store scanned results for future queries or user preferences.
    Tasks:
        Set up a database to save wine details and scores.
        Enable users to view their query history.

Tools and Skills Needed:

    Languages: Java (for backend database management).
    Database: PostgreSQL or SQLite.
    Timeline: 4 weeks.
    Learning Focus:
        Basics of SQL (writing queries, creating tables).
        Java database connectivity using JDBC.

Stage 7: Deploy the App

    Goal: Host the app online for users to access.
    Tasks:
        Set up a cloud server (e.g., AWS, Heroku).
        Configure a domain name and secure the site with HTTPS.

Tools and Skills Needed:

    Languages: Python (for Flask deployment).
    Platforms: Docker for containerization (optional).
    Timeline: 2–3 weeks.
    Learning Focus:
        Basics of web hosting and deployment.