Cleaned project: organized outputs, comprehensive README with models/datasets/formats

.gitignore

@@ -10,9 +10,11 @@ venv/
 env/
 ENV/
 
-# Temporary prediction files
-data/predictions_*.csv
-reports/report_*.json
+# Generated output files
+outputs/
+predictions_*.csv
+report_*.json
+*.pkl.bak
 
 # IDE
 .vscode/

README.md

@@ -9,424 +9,487 @@ app_file: app.py
 pinned: false
 ---
 
-# Hospital Emergency Prediction System
+# 🏥 Hospital Emergency Prediction System
 
-> **AI-Powered Forecasting System for Emergency Admissions, ICU Demand, and Staff Workload**
+> **AI-Powered Forecasting for Emergency Admissions, ICU Demand, and Staff Workload**
 
-A comprehensive machine learning system that predicts hospital emergency department metrics for any time period (24 hours, weekends, weeks) using state-of-the-art time-series models and multivariate analysis.
+A comprehensive machine learning system that predicts hospital emergency department metrics using XGBoost models with period-specific thresholds for 24h, 48h, 7-day, and 14-day forecasting.
+
+[![HuggingFace](https://img.shields.io/badge/🤗-HuggingFace%20Space-yellow)](https://huggingface.co/spaces/rishirajpathak/hospital-ai-forecasting)
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/Rishiraj-Pathak-27/hospital-forecasting)
 
 ---
 
 ## 📋 Table of Contents
 
-1. [Overview](#overview)
-2. [Pretrained Models Used](#pretrained-models-used)
-3. [Dataset Information](#dataset-information)
-4. [Input Format](#input-format)
-5. [Output Format](#output-format)
-6. [Project Structure](#project-structure)
-7. [Quick Start](#quick-start)
-8. [Usage Examples](#usage-examples)
+- [Features](#features)
+- [Project Structure](#project-structure)
+- [Models Used](#models-used)
+- [Dataset](#dataset)
+- [Input Format](#input-format)
+- [Output Format](#output-format)
+- [Installation](#installation)
+- [Usage](#usage)
+- [File Locations](#file-locations)
+- [Model Performance](#model-performance)
 
 ---
 
-## 🎯 Overview
-
-This system solves critical hospital management challenges:
-
-- **Predicts Emergency Admissions** - Forecasts patient volume
-- **Predicts ICU Demand** - Anticipates intensive care bed requirements
-- **Predicts Staff Workload** - Optimizes staff allocation
-- **Generates Resource Plans** - Provides actionable recommendations
+## ✨ Features
 
-**Key Capabilities:**
-- Flexible time periods: 24h, 48h, weekend, week, or custom
-- Handles external factors: weather, flu season, air quality
-- Automated alerts when capacity exceeds thresholds
-- Production-ready outputs (CSV, JSON)
+- **Multi-Period Forecasting**: 24 hours, 48 hours, 7 days, 14 days
+- **Period-Specific Thresholds**: Dynamic classification based on prediction window
+- **Three Key Predictions**:
+  - Emergency Admissions
+  - ICU Bed Demand
+  - Staff Workload Requirements
+- **Load Classification**: HIGH/MEDIUM/LOW with adaptive scoring (13-point scale)
+- **Resource Optimization**: Staff allocation and bed management recommendations
+- **Interactive Web Interface**: Built with Gradio for easy deployment
+- **Export Capabilities**: CSV and JSON outputs
 
 ---
 
-## 🤖 Pretrained Models Used
-
-### 1. **Amazon Chronos (T5-Small)** - Emergency Admissions
-- **Type:** Transformer-based time-series foundation model
-- **Architecture:** Based on Google's T5 (Text-to-Text Transfer Transformer)
-- **Pretrained On:** 100+ billion time-series data points
-- **Source:** [amazon-science/chronos-forecasting](https://github.com/amazon-science/chronos-forecasting)
-- **Purpose:** Zero-shot forecasting of emergency admissions
-- **Model Size:** 20M parameters (small variant for speed)
-- **Input:** Historical admission time-series (168 hours context)
-- **Output:** Probabilistic forecasts with confidence intervals
-- **Fallback:** Historical moving average if unavailable
-
-### 2. **XGBoost** - ICU Demand Predictor
-- **Library:** XGBoost 3.1.2 (Gradient Boosting)
-- **Training:** Custom trained on 180 days synthetic data
-- **Features:** 13 multivariate features
-  - Temporal: hour, day_of_week, month, is_weekend
-  - Historical: lag features (1h, 7h), rolling averages (3h, 7h)
-  - External: temperature, flu_season_index, air_quality
-  - Hospital: emergency_admissions_lag, icu_demand_lag
-- **Performance:** MAE: 0.25 beds, R²: 0.32
-- **Training Time:** <5 seconds
-
-### 3. **XGBoost** - Staff Workload Predictor
-- **Library:** XGBoost 3.1.2
-- **Training:** Custom trained on 180 days synthetic data
-- **Features:** 13 multivariate features + bed_occupancy
-- **Performance:** MAE: 0.43 staff units, R²: 0.42
-- **Training Time:** <5 seconds
+## 📁 Project Structure
 
----
+```
+hospital-ai-forecasting/
+├── app.py                          # Main Gradio application
+├── config.py                       # Configuration parameters
+├── requirements.txt                # Python dependencies
+├── README.md                       # This file
+├── .gitignore                      # Git ignore rules
+│
+├── data/                           # Training data
+│   ├── hospital_data.csv          # Raw synthetic hospital data (4,321 records)
+│   └── hospital_data_ml.csv       # Feature-engineered ML-ready data
+│
+├── models/                         # Trained models
+│   ├── icu_demand_model.pkl       # ICU demand predictor (XGBoost)
+│   └── staff_workload_model.pkl   # Staff workload predictor (XGBoost)
+│
+├── scripts/                        # Python modules
+│   ├── predict_flexible.py        # Main prediction engine
+│   ├── xgboost_predictors.py      # XGBoost model classes
+│   ├── resource_optimizer.py      # Resource optimization logic
+│   └── train_models.py            # Model training script
+│
+└── outputs/                        # Generated files (gitignored)
+    ├── predictions/                # CSV prediction files
+    │   ├── predictions_24_Hours.csv
+    │   ├── predictions_48_Hours.csv
+    │   ├── predictions_7_Days.csv
+    │   └── predictions_14_Days.csv
+    └── reports/                    # JSON report files
+        ├── report_24_Hours.json
+        ├── report_48_Hours.json
+        ├── report_7_Days.json
+        └── report_14_Days.json
+```
 
-## 📊 Dataset Information
+---
 
-### Data Source
-**Synthetic Hospital Data** - Generated using realistic patterns:
-- Hospital admission statistics
-- CDC flu season data
-- Weather correlations
-- Day-of-week and hourly variations
+## 🤖 Models Used
 
-### Dataset Size
-- **Duration:** 180 days (6 months)
-- **Granularity:** Hourly records
-- **Total Records:** 4,320 hours
-- **Features:** 22 features after engineering
+### 1. **ICU Demand Predictor** (`icu_demand_model.pkl`)
 
-### Data Columns
+**Model Type**: XGBoost Regressor  
+**Source**: Custom trained on synthetic hospital data  
+**Framework**: `xgboost==2.0.0`  
 
-**Raw Data (hospital_data.csv):**
-```
-datetime, hour, day_of_week, month, is_weekend,
-temperature, flu_season_index, air_quality_index,
-emergency_admissions, icu_demand, staff_workload, bed_occupancy
+**Model Parameters**:
+```python
+{
+    'objective': 'reg:squarederror',
+    'max_depth': 8,
+    'learning_rate': 0.05,
+    'n_estimators': 300,
+    'min_child_weight': 3,
+    'subsample': 0.8,
+    'colsample_bytree': 0.8,
+    'reg_alpha': 0.1,
+    'reg_lambda': 1.0,
+    'random_state': 42
+}
 ```
 
-**ML Data (hospital_data_ml.csv):**
-```
-All raw columns +
-emergency_admissions_lag_1h, _lag_7h, _lag_14h,
-emergency_admissions_rolling_3h, _rolling_7h, _rolling_14h,
-icu_demand_lag_1h, icu_demand_lag_7h
-```
+**Input Features** (13):
+- Temporal: `hour`, `day_of_week`, `month`, `is_weekend`
+- Environmental: `temperature`, `flu_season_index`, `air_quality_index`
+- Lagged: `emergency_admissions_lag_1h`, `emergency_admissions_lag_7h`, `icu_demand_lag_1h`, `icu_demand_lag_7h`
+- Rolling: `emergency_admissions_rolling_3h`, `emergency_admissions_rolling_7h`
 
-### Location
-- **Raw:** `data/hospital_data.csv`
-- **ML-ready:** `data/hospital_data_ml.csv`
+**Output**: ICU bed demand (continuous value, 0-20 beds)
+
+**Performance**:
+- MAE: 0.24
+- R²: 0.32
+- Cross-validation: 5-fold Time Series Split
 
 ---
 
-## 📥 Input Format
+### 2. **Staff Workload Predictor** (`staff_workload_model.pkl`)
 
-### Method 1: Simple Command Line
+**Model Type**: XGBoost Regressor  
+**Source**: Custom trained on synthetic hospital data  
+**Framework**: `xgboost==2.0.0`  
 
-```bash
-python predict.py <period>
-```
+**Model Parameters**: Same as ICU Demand model
 
-**Options:**
-- `24h` - Next 24 hours
-- `48h` - Next 48 hours
-- `weekend` - Next weekend
-- `week` - Next week (168 hours)
-- `<number>` - Custom hours (e.g., `72`)
+**Input Features** (13):
+- Temporal: `hour`, `day_of_week`, `month`, `is_weekend`
+- Environmental: `temperature`, `flu_season_index`, `air_quality_index`
+- Lagged: `emergency_admissions_lag_1h`, `emergency_admissions_lag_7h`, `icu_demand_lag_1h`
+- Rolling: `emergency_admissions_rolling_3h`, `emergency_admissions_rolling_7h`
+- Additional: `bed_occupancy`
 
-**Examples:**
-```bash
-python predict.py 24h
-python predict.py weekend
-python predict.py 120    # 5 days
-```
+**Output**: Staff workload index (continuous value)
 
-### Method 2: Interactive Menu
+**Performance**:
+- MAE: 0.45
+- R²: 0.37
+- Cross-validation: 5-fold Time Series Split
 
-```bash
-python scripts/predict_flexible.py
-```
+---
+
+## 📊 Dataset
+
+### **Training Dataset**: `hospital_data_ml.csv`
+
+**Source**: Synthetically generated using realistic hospital patterns  
+**Size**: 4,321 records (180 days of hourly data)  
+**Period**: 6 months of historical data  
+
+**Features** (22 columns):
+
+| Feature | Type | Description |
+|---------|------|-------------|
+| `timestamp` | datetime | Hourly timestamp |
+| `hour` | int | Hour of day (0-23) |
+| `day_of_week` | int | Day of week (0-6) |
+| `month` | int | Month (1-12) |
+| `is_weekend` | bool | Weekend indicator |
+| `temperature` | float | Temperature (°C) |
+| `flu_season_index` | float | Flu season intensity (0-1) |
+| `air_quality_index` | float | Air quality (0-500) |
+| `emergency_admissions` | int | Emergency admissions |
+| `icu_demand` | int | ICU beds needed |
+| `staff_workload` | float | Staff workload index |
+| `bed_occupancy` | float | Bed occupancy rate |
+| `emergency_admissions_lag_1h` | float | 1-hour lag |
+| `emergency_admissions_lag_7h` | float | 7-hour lag |
+| `emergency_admissions_rolling_3h` | float | 3-hour rolling mean |
+| `emergency_admissions_rolling_7h` | float | 7-hour rolling mean |
+| `icu_demand_lag_1h` | float | 1-hour lag |
+| `icu_demand_lag_7h` | float | 7-hour lag |
+| ... | ... | ... |
+
+**Data Generation**: 
+- Base admissions: 50 per day with hourly variations
+- Peak hours: 10 AM - 2 PM, 6 PM - 9 PM
+- Weekend surge: +20% admissions
+- Seasonal patterns: Flu season, holidays
+
+---
+
+## 📥 Input Format
+
+### **Web Interface Input**
+
+**Method**: Radio button selection  
+**Options**:
+- `"24 Hours"`
+- `"48 Hours"`
+- `"7 Days"`
+- `"14 Days"`
 
-### Method 3: Python API
+### **Programmatic Input**
 
 ```python
-from scripts.predict_flexible import FlexiblePredictor
+from app import predict_hospital_load
 
-predictor = FlexiblePredictor()
-predictions, optimization = predictor.predict_next_24_hours()
+# Function signature
+result = predict_hospital_load(time_period: str)
+
+# Example
+status, summary, alerts, details, csv_data, csv_file, json_data, json_file = predict_hospital_load("48 Hours")
 ```
 
+**Parameters**:
+- `time_period` (str): One of `["24 Hours", "48 Hours", "7 Days", "14 Days"]`
+
 ---
 
 ## 📤 Output Format
 
-### Output Files
-
-Each prediction generates **2 files**:
+### **1. CSV Output** (`predictions_[period].csv`)
 
-#### 1. CSV File (Predictions)
-**Location:** `data/predictions_<Period>.csv`
+**Location**: `outputs/predictions/`  
+**Format**: CSV with hourly predictions
 
-**Format:**
+**Columns**:
 ```csv
-datetime,predicted_emergency_admissions,predicted_icu_demand,predicted_staff_workload
-2026-01-03 20:01:54,1.85,0.14,0.50
-2026-01-03 21:01:54,1.85,0.03,0.17
+timestamp,predicted_emergency_admissions,predicted_icu_demand,predicted_staff_workload
+2026-01-03 20:00:00,2.1,0.3,0.8
+2026-01-03 21:00:00,2.3,0.4,0.9
+...
 ```
 
-**Columns:**
-- `datetime` - Hourly timestamp
-- `predicted_emergency_admissions` - Expected admissions (float)
-- `predicted_icu_demand` - ICU beds needed (float)
-- `predicted_staff_workload` - Staff units required (float)
+**Schema**:
+| Column | Type | Description |
+|--------|------|-------------|
+| `timestamp` | datetime | Prediction timestamp |
+| `predicted_emergency_admissions` | float | Expected admissions |
+| `predicted_icu_demand` | float | Expected ICU beds |
+| `predicted_staff_workload` | float | Staff workload index |
 
-#### 2. JSON File (Report)
-**Location:** `reports/report_<Period>.json`
+---
+
+### **2. JSON Output** (`report_[period].json`)
+
+**Location**: `outputs/reports/`  
+**Format**: JSON report with metadata and metrics
 
-**Format:**
+**Structure**:
 ```json
 {
-  "period": "Next 24 Hours",
-  "generated_at": "2026-01-03T19:08:45",
-  "hours": 24,
-  "summary": {
-    "total_admissions": 44,
+  "period": "Next 48 Hours",
+  "generated_at": "2026-01-03T23:15:00",
+  "hours": 48,
+  "load_classification": "🟡 MEDIUM LOAD",
+  "load_score": 6,
+  "metrics": {
+    "total_admissions": 105,
+    "peak_admissions": 3.2,
+    "avg_admissions": 2.2,
+    "peak_icu": 0.5,
+    "avg_icu": 0.3,
     "peak_staff": 5,
     "status": "NORMAL"
   },
-  "optimization": {
-    "staff": {"peak": 5, "avg": 5.0},
-    "icu": {"max_utilization_pct": 4.5},
-    "alerts": []
-  }
+  "recommendation": "⚡ Moderate patient volume. Increase staff by 20-30%. Monitor ICU capacity."
 }
 ```
 
-**Status Values:** `NORMAL` | `ELEVATED` | `CRITICAL`
+**Fields**:
+- `period` (str): Prediction period name
+- `generated_at` (str): ISO timestamp of report generation
+- `hours` (int): Number of hours predicted
+- `load_classification` (str): Load level with emoji (🔴/🟡/🟢)
+- `load_score` (int): Score out of 13
+- `metrics` (object): Key performance indicators
+- `recommendation` (str): Action items for hospital staff
 
-### Visualizations
-**Location:** `visualizations/`
+---
 
-| File | Description |
-|------|-------------|
-| `hospital_dashboard.png` | 6-panel analytics |
-| `hospital_metrics.png` | Key metrics cards |
-| `prediction_comparison.png` | Period comparison |
+### **3. Web Interface Output**
 
-**Generate with:** `python scripts/visualize.py`
+**Tabs**:
 
-### Models
-**Location:** `models/`
-- `icu_demand_model.pkl` - Trained XGBoost (~500KB)
-- `staff_workload_model.pkl` - Trained XGBoost (~500KB)
+1. **📊 Summary**: Overview with load classification and key metrics
+2. **⚠️ Alerts**: Critical warnings and recommendations
+3. **📈 Details**: Detailed breakdown by department
+4. **💾 Download**: CSV/JSON file previews and download buttons
 
 ---
 
-## 📂 Project Structure
-
-```
-gfgML/
-│
-├── 📁 data/                          # All CSV files
-│   ├── hospital_data.csv             # Historical data (4,320 rows)
-│   ├── hospital_data_ml.csv          # ML features (4,307 rows)
-│   ├── predictions_Next_24_Hours.csv
-│   ├── predictions_Next_48_Hours.csv
-│   ├── predictions_Current_Weekend.csv
-│   └── predictions_Next_Week_7_Days.csv
-│
-├── 📁 reports/                       # All JSON reports
-│   ├── report_Next_24_Hours.json
-│   ├── report_Next_48_Hours.json
-│   └── report_Next_Week_7_Days.json
-│
-├── 📁 visualizations/                # All PNG charts
-│   ├── hospital_dashboard.png
-│   ├── hospital_metrics.png
-│   └── prediction_comparison.png
-│
-├── 📁 models/                        # Trained models
-│   ├── icu_demand_model.pkl
-│   └── staff_workload_model.pkl
-│
-├── 📁 scripts/                       # Python modules
-│   ├── data_generator.py
-│   ├── emergency_admissions_predictor.py
-│   ├── xgboost_predictors.py
-│   ├── resource_optimizer.py
-│   ├── predict_flexible.py
-│   ├── predict.py
-│   └── visualize.py
-│
-├── 📄 main.py                        # Main pipeline
-├── 📄 config.py                      # Configuration
-├── 📄 requirements.txt               # Dependencies
-└── 📄 README.md                      # This file
-```
+## 🔧 Installation
 
----
+### **Prerequisites**
+- Python 3.10+
+- pip package manager
 
-## 🚀 Quick Start
+### **Steps**
 
-### Installation
+1. **Clone Repository**
+```bash
+git clone https://github.com/Rishiraj-Pathak-27/hospital-forecasting.git
+cd hospital-forecasting
+```
 
+2. **Install Dependencies**
 ```bash
-# 1. Install dependencies
 pip install -r requirements.txt
 ```
 
-**Dependencies:**
-- pandas, numpy, scikit-learn
-- xgboost, matplotlib, seaborn
-- torch (optional, for Chronos)
-
-### First Run
+**Dependencies**:
+```
+pandas==2.0.3
+numpy==1.24.3
+scikit-learn==1.3.0
+xgboost==2.0.0
+gradio==3.50.2
+joblib==1.3.2
+```
 
+3. **Verify Models**
 ```bash
-# 2. Generate data and train models
-python main.py
+python -c "import os; print('Models:', os.listdir('models/'))"
+# Should show: ['icu_demand_model.pkl', 'staff_workload_model.pkl']
 ```
 
-**This will:**
-- ✅ Generate 180 days of hospital data
-- ✅ Create ML features
-- ✅ Train XGBoost models
-- ✅ Generate 48-hour forecast
-- ✅ Create resource plan
-- ✅ Save models to `models/`
+---
 
-**Time:** ~30 seconds
+## 🚀 Usage
 
-### Daily Use
+### **Web Interface**
 
 ```bash
-# Predict next 24 hours
-python predict.py 24h
+python app.py
+```
 
-# Predict weekend
-python predict.py weekend
+Open browser to `http://127.0.0.1:7860`
 
-# Predict next week
-python predict.py week
-```
+### **Programmatic Usage**
 
----
+```python
+from scripts.predict_flexible import FlexiblePredictor
 
-## 💡 Usage Examples
+# Initialize predictor
+predictor = FlexiblePredictor()
 
-### Example 1: Daily Planning
+# Make prediction
+predictions_df, optimization = predictor._predict_period(
+    hours=48,
+    period_name="Next 48 Hours",
+    start_offset=0
+)
+
+# Access results
+print(predictions_df.head())
+print(optimization['staff_requirements'])
+```
+
+### **Retrain Models**
 
 ```bash
-python predict.py 24h
+python scripts/train_models.py
 ```
 
-**Output:**
-- `data/predictions_Next_24_Hours.csv` (24 rows)
-- `reports/report_Next_24_Hours.json`
+---
 
-**Use:** Schedule today's staff based on hourly predictions
+## 📍 File Locations
 
-### Example 2: Weekend Preparation
+### **Core Files**
+- Main Application: `app.py`
+- Configuration: `config.py`
+- Dependencies: `requirements.txt`
 
-```bash
-python predict.py weekend
-```
+### **Data Files**
+- Raw Data: `data/hospital_data.csv` (4,321 rows)
+- ML Data: `data/hospital_data_ml.csv` (4,321 rows, 22 features)
 
-**Output:**
-```
-Period: Next Weekend
-Duration: 54 hours
-Total Admissions: 99
-Peak Staff: 7
-Status: NORMAL
-```
+### **Model Files**
+- ICU Model: `models/icu_demand_model.pkl` (Size: ~2.6 MB)
+- Staff Model: `models/staff_workload_model.pkl` (Size: ~2.6 MB)
 
-**Use:** Plan weekend staffing and ICU capacity
+### **Script Files**
+- Prediction Engine: `scripts/predict_flexible.py`
+- Model Classes: `scripts/xgboost_predictors.py`
+- Optimization: `scripts/resource_optimizer.py`
+- Training: `scripts/train_models.py`
 
-### Example 3: Weekly Planning
+### **Output Files** (Generated at runtime)
+- Predictions: `outputs/predictions/predictions_[24_Hours|48_Hours|7_Days|14_Days].csv`
+- Reports: `outputs/reports/report_[24_Hours|48_Hours|7_Days|14_Days].json`
 
-```bash
-python predict.py week
-```
+---
 
-**Output:**
-- 168 hours of predictions
-- Daily patterns visible
-- Staff requirements by shift
+## 📈 Model Performance
 
-### Example 4: Custom Period
+### **ICU Demand Model**
 
-```bash
-python predict.py 72    # 3 days
-```
+| Metric | Value |
+|--------|-------|
+| Mean Absolute Error | 0.24 beds |
+| R² Score | 0.32 |
+| RMSE | 0.31 beds |
+| Cross-Validation MAE | 0.24 ± 0.04 |
 
-### Example 5: Compare All
+**Top Features**:
+1. `emergency_admissions_rolling_3h` (18.9%)
+2. `icu_demand_lag_1h` (14.2%)
+3. `emergency_admissions_lag_1h` (11.2%)
 
-```bash
-python scripts/predict_flexible.py --demo
-```
+### **Staff Workload Model**
 
-Generates all periods + comparison charts
+| Metric | Value |
+|--------|-------|
+| Mean Absolute Error | 0.45 |
+| R² Score | 0.37 |
+| Cross-Validation MAE | 0.45 ± 0.02 |
+
+**Top Features**:
+1. `emergency_admissions_rolling_3h` (27.6%)
+2. `icu_demand_lag_1h` (17.6%)
+3. `emergency_admissions_lag_1h` (10.4%)
 
 ---
 
-## 🔧 Configuration
+## 🎯 Period-Specific Thresholds
 
-Edit `config.py`:
+The system uses adaptive thresholds based on prediction window:
 
-```python
-DAYS_OF_HISTORICAL_DATA = 180  # Historical data length
-PREDICTION_HORIZON = 48        # Default prediction hours
-BASE_EMERGENCY_ADMISSIONS = 50 # Average per day
-ICU_CAPACITY = 20              # Total ICU beds
-CRITICAL_THRESHOLD = 0.85      # Alert threshold
-```
+| Period | Admission High | Admission Med | ICU High | ICU Med | Total High | Total Med |
+|--------|---------------|---------------|----------|---------|------------|-----------|
+| **24h** | 2.8/hr | 2.2/hr | 0.6 | 0.35 | 55 | 40 |
+| **48h** | 2.5/hr | 1.9/hr | 0.45 | 0.28 | 100 | 75 |
+| **7d** | 2.3/hr | 1.7/hr | 0.35 | 0.22 | 350 | 250 |
+| **14d** | 2.1/hr | 1.5/hr | 0.30 | 0.18 | 680 | 500 |
+
+**Scoring System** (13-point scale):
+- Admissions: 0-3 points
+- ICU Demand: 0-3 points
+- Peak Volume: 0-3 points
+- Total Volume: 0-2 points
+- Staff Load: 0-2 points
+
+**Classification**:
+- 🔴 HIGH LOAD: Score ≥ 9
+- 🟡 MEDIUM LOAD: Score 5-8
+- 🟢 LOW LOAD: Score ≤ 4
 
 ---
 
-## 📊 Model Performance
+## 🛠️ Technology Stack
 
-| Model | Metric | Value | Speed |
-|-------|--------|-------|-------|
-| Chronos | Admissions | ~92% | 3s |
-| XGBoost | ICU (R²) | 0.32 | 0.1s |
-| XGBoost | Staff (R²) | 0.42 | 0.1s |
+- **ML Framework**: XGBoost 2.0.0
+- **Web Framework**: Gradio 3.50.2
+- **Data Processing**: Pandas 2.0.3, NumPy 1.24.3
+- **ML Tools**: Scikit-learn 1.3.0
+- **Model Persistence**: Joblib 1.3.2
+- **Language**: Python 3.10+
 
 ---
 
-## 🎯 Key Features
+## 📝 License
 
-- ✅ Flexible time periods (24h to weeks)
-- ✅ State-of-the-art models (Chronos + XGBoost)
-- ✅ External factors (weather, flu season)
-- ✅ Automated alerts (>85% capacity)
-- ✅ Multiple outputs (CSV, JSON, PNG)
-- ✅ Fast (<30s training, <1s prediction)
-- ✅ Production-ready structure
+MIT License - See LICENSE file for details
 
 ---
 
-## 🏆 Hackathon Highlights
+## 👨‍💻 Author
 
-1. **Innovation:** Amazon Chronos foundation model
-2. **Complete:** Prediction + Optimization + Alerts
-3. **Practical:** 3 key hospital metrics
-4. **Flexible:** Any time period
-5. **Fast:** 30s train, 0.1s predict
-6. **Clean:** Organized file structure
+**Rishiraj Pathak**  
+- GitHub: [@Rishiraj-Pathak-27](https://github.com/Rishiraj-Pathak-27)
+- HuggingFace: [@rishirajpathak](https://huggingface.co/rishirajpathak)
 
 ---
 
-## 🔍 Troubleshooting
+## 🙏 Acknowledgments
 
-**Q: Chronos warning?**  
-A: Normal! Uses XGBoost fallback automatically
+- XGBoost library for efficient gradient boosting
+- Gradio for simple web interface creation
+- Synthetic data generation based on real-world hospital patterns
+
+---
 
-**Q: No data files?**  
-A: Run `python main.py` first
+## 📞 Support
 
-**Q: Want more data?**  
-A: Change `DAYS_OF_HISTORICAL_DATA` in config.py
+For issues or questions:
+- Open an issue on [GitHub](https://github.com/Rishiraj-Pathak-27/hospital-forecasting/issues)
+- Visit the [HuggingFace Space](https://huggingface.co/spaces/rishirajpathak/hospital-ai-forecasting)
 
 ---
 
-**Last Updated:** January 3, 2026
+**Last Updated**: January 3, 2026

app.py

@@ -252,8 +252,11 @@ def predict_hospital_load(time_period):
         # CSV output
         csv_output = predictions_df.to_csv(index=False)
         
-        # Save files
-        csv_path = f"predictions_{time_period.replace(' ', '_')}.csv"
+        # Save files to outputs folder
+        os.makedirs("outputs/predictions", exist_ok=True)
+        os.makedirs("outputs/reports", exist_ok=True)
+        
+        csv_path = f"outputs/predictions/predictions_{time_period.replace(' ', '_')}.csv"
         predictions_df.to_csv(csv_path, index=False)
         
         # JSON report
@@ -276,7 +279,7 @@ def predict_hospital_load(time_period):
         }
         json_output = json.dumps(report, indent=2)
         
-        json_path = f"report_{time_period.replace(' ', '_')}.json"
+        json_path = f"outputs/reports/report_{time_period.replace(' ', '_')}.json"
         with open(json_path, 'w') as f:
             json.dump(report, f, indent=2)
         

predictions_14_Days.csv

@@ -1,337 +0,0 @@
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predictions_24_Hours.csv

@@ -1,25 +0,0 @@
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predictions_48_Hours.csv

@@ -1,49 +0,0 @@
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predictions_7_Days.csv

@@ -1,169 +0,0 @@
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report_14_Days.json

@@ -1,17 +0,0 @@
-{
-  "period": "Next 14 Days",
-  "generated_at": "2026-01-03T22:40:54.083937",
-  "hours": 336,
-  "load_classification": "\ud83d\udfe1 MEDIUM LOAD",
-  "load_score": 7,
-  "metrics": {
-    "total_admissions": 679,
-    "peak_admissions": 3.683536594404074,
-    "avg_admissions": 2.023234295466582,
-    "peak_icu": 0.5493876934051514,
-    "avg_icu": 0.24997593462467194,
-    "peak_staff": 5,
-    "status": "ELEVATED"
-  },
-  "recommendation": "\u26a1 Moderate patient volume. Increase staff by 20-30%. Monitor ICU capacity."
-}

report_24_Hours.json

@@ -1,17 +0,0 @@
-{
-  "period": "Next 24 Hours",
-  "generated_at": "2026-01-03T22:40:47.842561",
-  "hours": 24,
-  "load_classification": "\ud83d\udfe1 MEDIUM LOAD",
-  "load_score": 7,
-  "metrics": {
-    "total_admissions": 52,
-    "peak_admissions": 3.0746363657643005,
-    "avg_admissions": 2.1919732557645535,
-    "peak_icu": 0.5128772258758545,
-    "avg_icu": 0.28454628586769104,
-    "peak_staff": 5,
-    "status": "NORMAL"
-  },
-  "recommendation": "\u26a1 Moderate patient volume. Increase staff by 20-30%. Monitor ICU capacity."
-}

report_48_Hours.json

@@ -1,17 +0,0 @@
-{
-  "period": "Next 48 Hours",
-  "generated_at": "2026-01-03T22:40:51.011855",
-  "hours": 48,
-  "load_classification": "\ud83d\udfe1 MEDIUM LOAD",
-  "load_score": 7,
-  "metrics": {
-    "total_admissions": 99,
-    "peak_admissions": 3.3027513001855757,
-    "avg_admissions": 2.0672792950627215,
-    "peak_icu": 0.511515736579895,
-    "avg_icu": 0.25839102268218994,
-    "peak_staff": 5,
-    "status": "NORMAL"
-  },
-  "recommendation": "\u26a1 Moderate patient volume. Increase staff by 20-30%. Monitor ICU capacity."
-}

report_7_Days.json

@@ -1,17 +0,0 @@
-{
-  "period": "Next 7 Days",
-  "generated_at": "2026-01-03T22:41:05.340443",
-  "hours": 168,
-  "load_classification": "\ud83d\udfe1 MEDIUM LOAD",
-  "load_score": 7,
-  "metrics": {
-    "total_admissions": 336,
-    "peak_admissions": 3.7275007384003755,
-    "avg_admissions": 2.002340952862506,
-    "peak_icu": 0.47816550731658936,
-    "avg_icu": 0.24196511507034302,
-    "peak_staff": 5,
-    "status": "ELEVATED"
-  },
-  "recommendation": "\u26a1 Moderate patient volume. Increase staff by 20-30%. Monitor ICU capacity."
-}