Update README.md

README.md

@@ -1,8 +1,8 @@
 ---
-title: StrokePatientsModel
-emoji: 🏢
-colorFrom: gray
-colorTo: gray
+title: StrokePainSideDetector
+emoji: 🧠
+colorFrom: red
+colorTo: blue
 sdk: streamlit
 sdk_version: 1.44.1
 app_file: app.py
@@ -10,56 +10,79 @@ pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🧠 Stroke Patient Pain Intensity Detector
 
+This app predicts the **pain intensity (PSPI score)** of stroke patients based on facial expressions using deep learning. It also detects the **affected side** of the face and uses the **unaffected half** for analysis.
 
+## 🚀 What This App Does
 
-# 🧠 Stroke Pain Detection App
+1. 🧠 **Detects affected side** of the face using a Keras CNN model.
+2. 🎯 **Crops the unaffected side** of the face using OpenCV + Haar cascades.
+3. 📈 **Predicts pain score (PSPI)** using a PyTorch ResNet model.
 
-A Streamlit-based app for automatically assessing pain intensity in stroke patients using facial asymmetry and deep learning.
+If the face is tilted, the app will automatically rotate it for correct alignment before prediction.
 
-This project uses three steps:
-1. **Detect the affected side** of a stroke patient's face using a Keras-based CNN model.
-2. **Crop the unaffected half** of the face.
-3. Use a PyTorch model to **predict the pain intensity (PSPI score)** from the expressive half.
+---
+
+## 🖼️ Input
+
+Upload a **full-face photo** of a stroke patient. The app:
+- Detects the face
+- Splits the face in half (face point of view)
+- Uses the expressive side for pain prediction
 
 ---
 
-## 🚀 How to Use This App
+## 📊 Output
+
+- **Affected Side** (face point of view)
+- **Unaffected Side**
+- **Predicted PSPI Score**: A number between 0 and 6
+- **Raw pain model output**
+- **Raw stroke model output** (value closer to 0 → left side affected; closer to 1 → right side affected)
+
+---
 
-Simply upload a **full-face image** of a stroke patient and let the model:
-- Detect the affected side
-- Crop the unaffected side
-- Predict the patient's pain intensity (PSPI)
+## 📚 PSPI Score Scale
 
-✅ You can access the live app here:  
-[🔗 Launch App →](https://huggingface.co/spaces/AdhamQQ/StrokePatientsModel)
+| Score | Pain Level        |
+|-------|-------------------|
+| 0     | No pain           |
+| 1–2   | Mild pain         |
+| 3–4   | Moderate pain     |
+| 5–6   | Severe pain       |
 
+The PSPI (Prkachin and Solomon Pain Intensity) score is based on facial action units like eye tightening, brow lowering, and cheek raising.
+
+---
 
 ## 💾 Models Used
 
-These models are automatically downloaded on first run:
+| Model                     | Task                            | Format         | Link                                                                 |
+|--------------------------|----------------------------------|----------------|----------------------------------------------------------------------|
+| `cnn_stroke_model.keras` | Detect affected facial side      | TensorFlow     | [Download](https://huggingface.co/AdhamQQ/cnn_stroke_model/resolve/main/cnn_stroke_model.keras) |
+| `pain_model.pth`         | Predict PSPI pain intensity      | PyTorch        | [Download](https://huggingface.co/AdhamQQ/cnn_stroke_model/resolve/main/pain_model.pth)         |
+
+---
+
+## 🛠️ Technologies
 
-| Model                             | Description                            | Link                                                                 |
-|----------------------------------|----------------------------------------|----------------------------------------------------------------------|
-| `cnn_stroke_model.keras`         | Detects the affected side of the face  | [Download](https://huggingface.co/AdhamQQ/cnn_stroke_model/resolve/main/cnn_stroke_model.keras)         |
-| `right_side_pain_model.pth`      | Predicts pain intensity (PSPI score)   | [Download](https://huggingface.co/AdhamQQ/cnn_stroke_model/resolve/main/right_side_pain_model.pth)      |
+- **Streamlit** for the web interface
+- **OpenCV** for face detection
+- **TensorFlow/Keras** for stroke side classification
+- **PyTorch** for PSPI pain intensity prediction
 
 ---
 
-## 🧠 Project Goals
+## 🤝 Credits
 
-This app is part of a larger system to:
-- Automatically detect which side of a stroke patient's face is impaired
-- Use the **expressive side** to analyze pain using PSPI (Prkachin and Solomon Pain Intensity) scores
+Developed by [Your Name or Team Name]  
+With support from Hugging Face Spaces
 
 ---
 
-## ✨ Credits
+## 🔗 Live App
 
-using:
-- Streamlit
-- TensorFlow/Keras
-- PyTorch
-- Hugging Face Hub
+Launch it on Hugging Face Spaces:  
+[👉 Try It Now](https://huggingface.co/spaces/YourUsername/StrokePainSideDetector)