Pushing the code for huggingface deployment again

README.md

@@ -1,31 +1,127 @@
 ---
-title: Drive Paddy
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
+title: Drive Paddy - Drowsiness Detection
+emoji: 🚗
+colorFrom: green
+colorTo: blue
+sdk: streamlit
+app_file: drive_paddy/main.py
 pinned: false
-short_description: Drive Paddy is a drowsiness detection buddy for drivers, utilizing OpenCV and a fine-tuned CNN model to monitor driver alertness.
 ---
 
-# Drive Paddy 🚀
+<div align="center">
+  <img src="https://em-content.zobj.net/source/samsung/380/automobile_1f697.png" alt="Car Emoji" width="100"/>
+  <h1>Drive Paddy</h1>
+  <p><strong>Your AI-Powered Drowsiness Detection Assistant</strong></p>
+  
+  <p>
+    <a href="#"><img alt="License" src="https://img.shields.io/badge/License-MIT-yellow.svg"/></a>
+    <a href="#"><img alt="Python" src="https://img.shields.io/badge/Python-3.9+-blue.svg"/></a>
+    <a href="#"><img alt="Streamlit" src="https://img.shields.io/badge/Streamlit-1.35.0-red.svg"/></a>
+  </p>
 
-Drive Paddy is a system designed to enhance driver safety by detecting drowsiness. It utilizes **OpenCV** for real-time computer vision tasks and a **fine-tuned Convolutional Neural Network (CNN)** model to monitor driver alertness and help prevent fatigue-related incidents.
+  <p>A real-time system to enhance driver safety by detecting signs of drowsiness using advanced computer vision and deep learning techniques.</p>
 
-## Core Functionality
+  <!-- *A GIF of the application running would be highly effective here.*
+  
+  **[GIF of Drive Paddy in Action]** -->
+</div>
 
-*   **Drowsiness Detection:** Identifies signs of driver fatigue by analyzing visual cues from a camera feed, such as eye closure duration and head pose.
-*   **Real-time Monitoring:** Continuously processes video input to assess the driver's alertness level.
-*   **Computer Vision Engine:** Leverages OpenCV for robust facial feature detection and eye state analysis.
-*   **AI-Powered Classification:** Employs a fine-tuned CNN model for accurate determination of drowsiness.
+---
+
+## 🌟 Features
+
+Drive Paddy employs a sophisticated, multi-faceted approach to ensure robust and reliable drowsiness detection.
+
+-   **Hybrid Detection Strategy**: Combines traditional computer vision techniques with deep learning for superior accuracy.
+-   **Multi-Signal Analysis**:
+    -   **👀 Eye Closure Detection**: Measures Eye Aspect Ratio (EAR) to detect prolonged blinks and microsleeps.
+    -   **🥱 Yawn Detection**: Measures Mouth Aspect Ratio (MAR) to identify driver fatigue.
+    -   **😴 Head Pose Estimation**: Tracks head pitch and yaw to detect nodding off or inattentiveness.
+    -   **🧠 Deep Learning Inference**: Utilizes a pre-trained `EfficientNet-B7` model for an additional layer of analysis.
+-   **Dynamic AI-Powered Alerts**: Leverages the Gemini API and gTTS for clear, context-aware voice alerts that are played directly in the user's browser.
+-   **Low-Light Warning**: Automatically detects poor lighting conditions that could affect detection accuracy and notifies the user.
+-   **Web-Based Interface**: Built with Streamlit for a user-friendly and accessible experience.
+-   **Configurable**: All detection thresholds and model weights can be easily tuned via a central `config.yaml` file.
+
+---
+
+## 🛠️ How It Works
+
+The system processes a live video feed from the user's webcam and calculates a weighted "drowsiness score" based on the configured detection strategy.
+
+1.  **Video Processing**: The `streamlit-webrtc` component captures the camera feed.
+2.  **Concurrent Detection**: The `HybridProcessor` runs two pipelines in parallel for maximum efficiency:
+    -   **Geometric Analysis (`geometric.py`)**: Uses `MediaPipe` to detect facial landmarks and calculate EAR, MAR, and head position in real-time.
+    -   **Deep Learning Inference (`cnn_model.py`)**: Uses a `dlib` face detector and a `PyTorch` model to classify the driver's state. This is run on a set interval to optimize performance.
+3.  **Scoring & Alerting**: The results are weighted and combined. If the score exceeds a set threshold, an alert is triggered.
+4.  **AI Voice Generation**: The `GeminiAlertSystem` sends a prompt to the Gemini API, generates a unique voice message using `gTTS`, and sends the audio data to the browser for playback.
+
+---
+
+## 🚀 Setup and Installation
+
+Follow these steps to set up and run Drive Paddy on your local machine.
+
+### 1. Clone the Repository
+
+```bash
+git clone https://github.com/<dev-tyta>/drive-paddy.git
+cd drive-paddy
+```
+
+### 2. Set Up a Virtual Environment (Recommended)
+
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
+```
+
+### 3. Install Dependencies
+
+Install all required Python packages from the `requirements.txt` file.
+
+```bash
+pip install -r requirements.txt
+```
+
+### 4. Download the CNN Model
+
+Run the provided script to download the pre-trained model from Hugging Face Hub.
+
+```bash
+python download_model.py
+```
+
+### 5. Configure Environment Variables
+
+Create a `.env` file by copying the example file.
+
+```bash
+cp .env.example .env
+```
+
+Now, open the `.env` file and add your Gemini API key:
+
+```
+GEMINI_API_KEY="YOUR_GEMINI_API_KEY_GOES_HERE"
+```
+
+---
+
+## ⚙️ Configuration
+
+The application's behavior can be fine-tuned via the `config.yaml` file. You can adjust detection thresholds, change the detection strategy (`geometric`, `cnn_model`, or `hybrid`), and modify the weights for the hybrid scoring system without touching the source code.
+
+---
+
+## ▶️ Usage
+
+To run the application, execute the following command from the root directory of the project:
 
-## Technology Stack
+```bash
+streamlit run drive_paddy/main.py
+```
 
-*   **Computer Vision:** OpenCV
-*   **Deep Learning Model:** Fine-tuned Convolutional Neural Network (CNN)
-*   **Application Framework:** Streamlit (as indicated by project metadata `tags`)
-*   **Containerization:** Docker (as indicated by project metadata `sdk`)
+Open your web browser and navigate to the local URL provided by Streamlit (usually `http://localhost:8501`).
 
+---