LSTM Traffic Forecast API 🧠📈

This repository contains the backend microservice for the LSTM (Future Forecaster) model, a core component of the "Traffic Flow Prediction" project.

The purpose of this API is to receive a location and a future date, load a pre-trained Keras LSTM model, and return a 24-hour time-series forecast for traffic congestion.

🚀 Role in Project Architecture

This service is not intended to be called directly by the public. It is a backend microservice designed to be called by the main Node.js API Gateway.

Data Flow: Frontend ➔ Node.js Gateway ➔ This LSTM API

📦 API Endpoints

1. Health Check

• Endpoint: GET /
• Description: A simple endpoint to confirm the API is running.
• Response (200 OK):

  { "message": "LSTM Traffic Forecast API is running!" }

2. Make Forecast

• Endpoint: POST /predict/
• Description: Receives location and date, then generates a 24-hour forecast.
• Request Body:

  {
    "model": "lstm",
    "coordinates": { "lat": 12.9716, "lng": 77.5946 },
    "selectedDate": "2025-11-20"
  }

• Success Response (200 OK):
  • Returns the labels (labels) and predicted values (data) our frontend line chart expects.

  {
    "labels": ["00:00", "01:00", "02:00", ... , "23:00"],
    "data": [0.1, 0.1, 0.2, ... , 0.3, 0.2]
  }

• Error Response (500):
  • Returns an error if the model or scaler files are not loaded.

  { "detail": "Model or scaler is not available" }

🛠️ Setup and Installation

Here are the step-by-step commands to set up and run this server locally.

1. Clone the Repository

# Clone this repository
git clone https://github.com/PrajwalShetty-114/LSTM-Model.git
cd traffic-prediction-lstm-api

2. Set Up Virtual Environment

# Create a virtual environment
python -m venv .venv

# Activate it
# On Windows:
.\.venv\Scripts\activate
# On Mac/Linux:
source .venv/bin/activate

3. Install Git LFS

This project uses Git Large File Storage (LFS) for the .keras and .pkl model files.

# Install Git LFS (if not already installed)
git lfs install

# Pull the large files from LFS storage
git lfs pull

Part 2: Install Dependencies

All required packages are listed in `requirements.txt`.

# Install all required Python libraries
pip install -r requirements.txt

Part 3: Run the Server

This API is configured to run on **port 8002** to avoid conflicts with other project services.

# Run the FastAPI server with auto-reload
uvicorn main:app --reload --port 8002

The server will be available at http://127.0.0.1:8002.

Part 4: Model Details

• Model: data/lstm_traffic_model.keras
  • A Keras/TensorFlow Sequential model trained in Google Colab.
• Scaler: data/lstm_scaler.pkl
  • An sklearn.preprocessing.MinMaxScaler object.
  • This is crucial as it's used to scale the input data (0-1) before prediction and inverse-transform the output (0-1) back into a real traffic value.