README.md

metadata

title: 🏏 IPL Match Predictor & Simulator
emoji: 🏏
colorFrom: yellow
colorTo: blue
sdk: streamlit
sdk_version: 1.30.0
app_file: app.py
pinned: false
license: mit
tags:
  - cricket
  - ipl
  - deep-learning
  - streamlit
  - gru
  - prediction
  - sports
  - commentary

# 🏏 IPL Match Predictor & Live Simulation App

Welcome to the **IPL Match Predictor** -- a powerful and interactive Streamlit-based application that simulates and predicts IPL match outcomes using machine learning, deep learning, and Generative AI commentary.

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## 🚀 Key Features

### 🎯 1. IPL Score Prediction Dashboard (GRU-Based) - Predicts final match score using a GRU deep learning model. - Inputs: Over-wise runs and optional wickets (20 overs). - Model trained on synthetic match data to mimic real T20 dynamics.

### 🧠 2. GPT Commentary Generator (RAG-based, Optional) - Generate multi-turn AI commentary based on match progression. - Integrates GPT-3.5 (OpenAI) with retrieval-based chunked summaries. *(Optional deployment upgrade)*

### 🧮 3. Match Scenario Simulator - Simulate custom match scenarios based on user-input cumulative scores. - Get final score predictions even with mid-match inputs.

### 📊 4. IPL Match Simulation + Points Table Logic - Team vs Team match generation with venue and status (Completed/In Progress/Scheduled). - Dynamic scorecard visualization with runs, wickets, and predicted outcomes.

### 📈 5. Live Match Predictor - Simulate full match: generates random over-wise runs + wickets. - Visualizes match progression and predicts final score with model confidence.

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## 📁 Project Structure

final_app/ │ ├── app.py # ✅ Unified Streamlit app ├── requirements.txt # ✅ Dependencies for Hugging Face ├── trained_model/ # ✅ GRU-based models + scalers ├── match_live_predictor/ # ✅ Live match predictor files ├── match_simulator.py # Scenario-based simulator ├── *.h5 / .keras # DL models (GRU, BiLSTM, CNN, LSTM) ├── scaler_.save # Input/output scalers ├── *.csv # Match data for commentary, points, visuals ├── *.gif # Dashboard animations └── *.ipynb # Phase notebooks and analysis

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## 🔧 Requirements

Install dependencies using:

```bash pip install -r requirements.txt

Model Info Model: GRU-based sequence predictor

Input: 20 timesteps (Runs, Wickets)

Output: Scaled prediction of final score

Scaler: MinMaxScaler fitted on input & target

GPT Commentary (Optional) To enable RAG-based GPT-3.5 commentary:

Set your OPENAI_API_KEY in Hugging Face secrets.

Add commentary loader and chunker for match summaries.

🧑‍💻 Built By 👨‍💻 Dinesh Kumar | Powered by Streamlit, TensorFlow, NumPy, Hugging Face, and Ope