Update README.md

Author: Hariprashad-Ravikumar

README.md

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 # AI-DataScience-Lab
 
-A web application for data science workflows, allowing users to upload datasets, clean and analyze them using Pandas and scikit-learn (with future TensorFlow integration), powered by a Flask backend.
-
-## Features
-- File upload interface
-- Data cleaning with Pandas
-- Initial analysis and predictions using scikit-learn
-- Ready for AI model integration via OpenAI API or TensorFlow
-
-## Tech Stack:
-- Front-end: HTML, Bootstrap (optional)
-- Back-end: Python, Flask
-- Data processing: Pandas, scikit-learn
-- Deployment: Render / GitHub Pages
+**AI-DataScience-Lab** is an end-to-end forecasting web application designed to upload CSV datasets, clean and analyze them using Python libraries, generate visualizations and predictive models with `scikit-learn`, and summarize the dataset using OpenAI’s GPT-3.5 API.
+
+The frontend is hosted on **GitHub Pages**, and the backend is deployed on **Azure App Service**, creating a scalable and professional architecture suitable for real-world AI and data science workflows.
+
+---
+
+## 🌐 Live Demo
+
+- **Frontend (GitHub Pages):** [https://hariprashad-ravikumar.github.io/AI-DataScience-Lab/](https://hariprashad-ravikumar.github.io/AI-DataScience-Lab/)
+- **Backend (Azure):** [https://ai-dslab-backend-cpf2feachnetbbck.westus-01.azurewebsites.net/](https://ai-dslab-backend-cpf2feachnetbbck.westus-01.azurewebsites.net/)
+
+---
+
+## ⚙️ Features
+
+- Upload CSV files with two columns: `X` (dates) and `Y` (numerical values)
+- Cleans data using `pandas`, removes invalid entries
+- Generates a scatter plot using `matplotlib`
+- Converts date strings to ordinal format and trains a `LinearRegression` model with `scikit-learn`
+- Uses **OpenAI API** (GPT-3.5-turbo) to summarize the uploaded dataset
+- Predicts future `Y` values for user-supplied future `X` (date) values
+- Secure HTTPS communication across GitHub and Azure (CORS-enabled)
+- Temporary file storage using Python's `tempfile`, cleaned automatically on restart
+
+---
+
+## 📊 Technical Workflow
+
+### 1. **Frontend (GitHub Pages)**
+
+- HTML + JavaScript app with forms to:
+  - Upload CSV data
+  - Request future predictions
+- Communicates with the backend via `fetch()` using HTTPS POST requests
+- Displays:
+  - Processing log
+  - OpenAI-generated summary
+  - Forecast output
+  - Auto-generated plot image
+
+### 2. **Backend (Azure App Service - Python Flask)**
+
+- **Routes:**
+  - `POST /upload`: Handles file uploads, data cleaning, modeling, summary generation
+  - `POST /predict`: Accepts future dates, returns predictions
+  - `GET /plot.png`: Serves saved scatter plot image
+
+### 3. **Processing Pipeline**
+
+- **Step 1: Data Cleaning**
+  - Reads CSV using `pandas`
+  - Drops NA values and converts `X` to datetime format
+
+- **Step 2: Visualization**
+  - Uses `matplotlib` to generate scatter plot
+  - Plot saved to a temporary directory and served on request
+
+- **Step 3: Modeling**
+  - Uses `scikit-learn` `LinearRegression` to fit `X` (date ordinal) → `Y`
+  - Model used to predict future values based on user input
+
+- **Step 4: Summarization**
+  - Sends cleaned dataset (via `.head(10).to_csv()`) to OpenAI GPT-3.5 API
+  - Summary generated and returned to frontend
+
+---
+
+## 🛠️ Tech Stack
+
+| Layer     | Technology                               |
+|-----------|-------------------------------------------|
+| Frontend  | HTML, JavaScript, GitHub Pages            |
+| Backend   | Flask, Azure App Service                  |
+| ML Tools  | `pandas`, `scikit-learn`, `matplotlib`    |
+| AI        | OpenAI GPT-3.5 (`openai` Python SDK)      |
+| Storage   | Python `tempfile` for secure cleanup      |
+| Deployment| Gunicorn + Azure Linux App Container      |
+
+---
+
+## 🔐 Security and Performance
+
+- Uses `flask-cors` to securely allow cross-origin requests from GitHub Pages
+- All requests are served over HTTPS
+- Files and plots are saved temporarily and deleted automatically on app shutdown using `tempfile.TemporaryDirectory` and `atexit`
+
+---
+
+## 🚀 How to Run Locally
+
+1. **Clone the repo**:
+   ```bash
+   git clone https://github.com/Hariprashad-Ravikumar/AI-DataScience-Lab.git
+   cd AI-DataScience-Lab/backend