Update README.md

README.md

@@ -1,16 +1,39 @@
-## 🩸 **Cycle Sync: Menstrual Cycle Prediction using LSTM**
-
-### 🚀 **Model Overview**
-The `cycle-sync` model is built using a Long Short-Term Memory (LSTM) architecture trained to predict menstrual cycle lengths and period durations based on a user’s past period history. 
-
-### 🔥 **Model Highlights**
-- 🧠 **Architecture:** LSTM (Long Short-Term Memory) with time-series inputs.  
-- 📊 **Purpose:** Predict the next period start date and duration based on previous cycle data.  
-- 🎯 **Task Type:** `time-series-forecasting`  
-- 📚 **Framework:** Keras with TensorFlow backend.  
-- 📈 **Scalers:** `MinMaxScaler` used for feature and label scaling.  
-
 ---
+language: en
+license: apache-2.0
+tags:
+  - keras
+  - tensorflow
+  - time-series
+  - menstrual-cycle-prediction
+  - healthcare
+pipeline_tag: time-series-forecasting
+model-index:
+  - name: lstm_combined_model
+    results:
+      - task:
+          type: time-series-forecasting
+          name: Menstrual Cycle Prediction
+        metrics:
+          - type: mae
+            value: 1.2
+            name: Mean Absolute Error (MAE)
+          - type: mse
+            value: 2.5
+            name: Mean Squared Error (MSE)
+---
+
+# 🩸 **Cycle Sync: Menstrual Cycle Prediction using LSTM**
+
+## 🚀 **Model Overview**
+The `cycle-sync` model is built using a Long Short-Term Memory (LSTM) architecture trained to predict menstrual cycle lengths and period durations based on a user’s past period history.
+
+## 🔥 **Model Highlights**
+- 🧠 **Architecture:** LSTM (Long Short-Term Memory) with time-series inputs.
+- 📊 **Purpose:** Predict the next period start date and duration based on previous cycle data.
+- 🎯 **Task Type:** `time-series-forecasting`
+- 📚 **Framework:** Keras with TensorFlow backend.
+- 📈 **Scalers:** `MinMaxScaler` used for feature and label scaling.
 
 ## 📡 **Usage**
 
@@ -32,86 +55,4 @@ with open("feature_scaler.pkl", "rb") as f:
 
 with open("label_scaler.pkl", "rb") as f:
     label_scaler = pickle.load(f)
-```
-
----
-
-## 📊 **Input & Output Schema**
-
-### 📥 **Input Format**
-The model expects a 3D input of shape:
-```
-(1, 3, 2)
-```
-- **3**: Number of previous cycles used for prediction.  
-- **2**: Features - cycle length and period duration.
-
-### 📤 **Output Format**
-The model outputs a 1D array:
-```
-[cycle_length, period_duration]
-```
-- `cycle_length`: Predicted length of the next cycle.  
-- `period_duration`: Predicted duration of the next period.  
-
----
-
-## 🎯 **Prediction Example**
-
-```python
-# Example input: 3 cycles with lengths and durations
-last_three_cycles = np.array([[[28, 5], [29, 5], [30, 6]]])
-
-# Scale the input
-input_flat = last_three_cycles.reshape(-1, 2)
-input_scaled_flat = feature_scaler.transform(input_flat)
-input_scaled = input_scaled_flat.reshape(1, 3, 2)
-
-# Make prediction
-prediction_scaled = model.predict(input_scaled)
-prediction = label_scaler.inverse_transform(prediction_scaled)[0]
-
-# Extract predicted values
-next_cycle_length = round(prediction[0])
-next_period_duration = round(prediction[1])
-
-print(f"Predicted Next Cycle Length: {next_cycle_length} days")
-print(f"Predicted Period Duration: {next_period_duration} days")
-```
-
----
-
-## 📚 **Training Details**
-- **Data Sources:** Synthetic and real menstrual cycle data.  
-- **Model Architecture:** 2-layer LSTM with dropout and dense output.  
-- **Loss Function:** Mean Squared Error (MSE)  
-- **Optimizer:** Adam  
-
----
-
-## 📊 **Evaluation Metrics**
-- 📏 **MAE:** ~1.2  
-- 📏 **MSE:** ~2.5  
-
----
-
-## 🔒 **License**
-This model is licensed under the Apache 2.0 License.  
-
----
-
-## 🤝 **Contributing**
-We welcome contributions! Please open an issue or submit a pull request if you’d like to improve the model or documentation.  
-
----
-
-## ❤️ **Acknowledgements**
-Special thanks to the healthcare community and menstrual health advocates who contributed to building this predictive model.
-
----
-
-🎉 **Ready to predict and empower users with data-driven cycle predictions!** 💡
-
----
-
-Let me know if you need help uploading or tweaking this! 🚀😊
+```