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| 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. | |
| \-\-- | |
| \## 🚀 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. | |
| \-\-- | |
| \## 📁 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 | |
| \-\-- | |
| \## 🔧 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 | |