Update README.md

README.md

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 ---
 license: mit
-language:
-- en
-metrics:
-- accuracy
-pipeline_tag: time-series-forecasting
-tags:
-- finance
-- GAN
-- PYTORCH
----
+title: Synthetic Stock Data Generator & Visualizer
+emoji: 🧠
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.0
+app_file: app/app.py
+pinned: false
+---
+# 🧠 Synthetic Stock Data Generator & Visualizer
+
+This project builds a **synthetic stock market data generator** using a combination of **Autoencoders (AE)** and **Generative Adversarial Networks (GANs)**.  
+The goal is to create realistic synthetic financial time-series data and compare model performance between **real** and **synthetic** datasets.
+
+---
+
+## 🚀 **Project Overview**
+
+### 🔹 Workflow
+1. **Autoencoder (AE):**
+   - Encodes stock price data into a compressed **latent space**.
+   - Captures temporal and feature-based dependencies between Open, High, Low, Close, and Volume.
+
+2. **GAN (Generator + Discriminator):**
+   - Learns to generate **synthetic latent vectors** that mimic the AE latent representations.
+   - Generator produces fake latent vectors.
+   - Discriminator learns to distinguish between real (from AE encoder) and fake (from Generator).
+
+3. **Synthetic Data Reconstruction:**
+   - The **synthetic latent vectors** are passed through the **AE Decoder**.
+   - This recreates **synthetic stock market data** at the feature level (Open, High, Low, Close, Volume).
+
+4. **Model Evaluation:**
+   - A downstream **neural network classifier** is trained on:
+     - Real data
+     - Synthetic data
+   - Performance metrics and comparison charts are saved in the `/charts` folder.
+
+---
+
+## 📊 **Visualization App**
+
+The project includes a **Gradio-powered dashboard** to visualize stock time series for real and synthetic data.
+
+### 🖥️ Try it on Hugging Face
+If you’re viewing this on Hugging Face, launch the app directly below 👇
+
+[![Hugging Face Space](https://img.shields.io/badge/Gradio_App-Open_in_Space-blue?logo=gradio)](https://huggingface.co/spaces/Raheel31/Synthetic_Stock_Data)
+
+### 🔍 App Features
+- Select any stock ticker and feature (Open, High, Low, Close, Volume)
+- View **5-year time series** comparisons of **original vs synthetic data**
+- Interactive plots rendered with `matplotlib`
+
+---
+
+## 📂 **Repository Structure**
+