Update README.md

README.md

@@ -1,20 +1,43 @@
 ---
 title: FacialKeypointDetectionCNN
-emoji: 🚀
+emoji: 🙂
 colorFrom: red
 colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
+sdk: streamlit
+app_file: src/streamlit_app.py
 pinned: false
 short_description: 'Predict facial keypoints from an uploaded face image '
 license: mit
 ---
 
-# Welcome to Streamlit!
+# 🙂 Facial Keypoints Detection (CNN)
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+This app predicts **facial keypoints** (eyes, eyebrows, nose, mouth) from a face image.
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+## What this project does
+- Input: face image (grayscale or RGB)
+- Output: 30 values (x/y coordinates for 15 facial keypoints)
+- The app overlays keypoints on a **96×96** image and shows the coordinates in a table.
+
+## Files in this repo
+- `app.py` → Streamlit app
+- `final_keypoints_cnn.keras` → trained Keras model
+- `target_cols.json` → output column names (order of the 30 targets)
+- `preprocess_config.json` → preprocessing settings (image size, normalization)
+
+## How to run locally
+```bash
+pip install -r requirements.txt
+streamlit run app.py
+Preprocessing (same as training)
+Convert to grayscale
+
+Resize to 96×96
+
+Normalize pixels: x / 255.0
+
+Model predicts normalized coordinates
+
+Convert back to pixel space: y = y * 48 + 48
+
+Clip to valid range: [0, 96]