Palcidux Project Documentation

Project Overview

Palcidux offers three major AI-driven services:

1. Image-Based Question Answering (Prompt-Based Image Summarization): Users input an image along with a related query, and the system provides an answer based on the content of the image.
2. Prompt-Based Text Generation: Users input a text prompt, and the system generates coherent and contextually relevant text.
3. Car Price Prediction: Users input the car's brand and model name, and the system predicts the car's price by retrieving other features from the backend database.

Additionally, Palcidux provides AI-related blogs for users to explore through designated endpoints.

The frontend is built using TailwindCSS, providing a modern and responsive UI, while the backend is powered by Flask, which handles all API requests and business logic. The car price prediction is driven by a Decision Tree Regressor model.

Features

1. Image-Based Question Answering (Prompt-Based Image Summarization)

• Functionality: Users upload an image and ask a query related to that image. The system processes the image and responds with an answer based on the content.
• Technology: Flask manages the input and passes the image and query to an AI model capable of image summarization and answering questions based on image content.
• API Endpoint: /image
• User Input: Image (file) and query (text)
• User Output: Answer related to the image.

2. Prompt-Based Text Generation

• Functionality: Users can generate text by providing a prompt. This service is useful for content creation, storytelling, and writing assistance.
• Technology: Flask connects user input with a language model that generates text based on the prompt.
• API Endpoint: /text-gen
• User Input: Prompt (text)
• User Output: Generated text.

3. Car Price Prediction

• Functionality: Users input only the car's brand and model. The system predicts the car’s price by retrieving other features (such as year and mileage) from the backend.
• Machine Learning Model: A Decision Tree Regressor is used for predicting car prices. This model has been trained on car features and historical price data.
• API Endpoint: /carprice
• User Input: Car brand and model (text)
• User Output: Predicted price of the car.

4. AI Blogs

• Functionality: Palcidux also offers AI-related blogs, allowing users to read up on topics related to artificial intelligence.
• API Endpoint: /blog-{blogname}
• User Input: Blog name in the URL.
• User Output: Blog content.

Technology Stack

Frontend

• TailwindCSS: TailwindCSS is used to design a responsive and modern user interface. It enables the rapid styling of the application's components.

Backend

• Flask: Flask is used for managing API requests, routing, and integration with machine learning models. It serves the core functionality for the image, text, and car price prediction services.

Machine Learning Model

• Decision Tree Regressor: This model powers the car price prediction service. It is trained using a dataset of car prices and their associated features.

How the System Works

1. User Interface: Users interact with Palcidux through a web-based interface built with TailwindCSS.
2. Backend Services: Flask handles all the backend logic, managing API requests for image-based question answering, text generation, and car price prediction, as well as serving blog content.
3. Machine Learning: For car price prediction, the Decision Tree Regressor model retrieves the necessary data and predicts prices based on user inputs and backend information.

API Endpoints

1. Image-Based Question Answering

• URL: /image
• Method: POST
• Input: Form data with image file and query (text)
• Output: Answer related to the image content.

2. Text Generation

• URL: /text-gen
• Method: POST
• Input: JSON { "prompt": "text prompt" }
• Output: Generated text.

3. Car Price Prediction

• URL: /carprice
• Method: POST
• Input: JSON { "brand": "car brand", "model": "car model" }
• Output: Predicted price of the car.

4. AI Blogs

• URL: /blog-{blogname}
• Method: GET
• Input: URL parameter (blog name).
• Output: Blog content related to artificial intelligence.

Installation and Setup

Prerequisites

• Python 3.x
• Flask
• scikit-learn
• TailwindCSS

Steps

1. Clone the Repository

   git clone <repository-url>

2. Install Backend Dependencies

   pip install -r requirements.txt

3. Run the Flask Backend

   python app.py

4. Frontend Setup Install and configure TailwindCSS as per the official documentation TailwindCSS Installation Guide.

5. Train the Car Price Prediction Model (if needed) If retraining the model is required:

   from sklearn.tree import DecisionTreeRegressor
   # Load your dataset
   # Split data and train the model
   model = DecisionTreeRegressor()
   model.fit(X_train, y_train)