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README.md

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+Here’s a clean, detailed, and ready-to-go `README.md` for your Hugging Face repo! 📚✨
+
 ---
-pipeline_tag: time-series-forecasting
----
+
+## 🩸 **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**
+
+### 🎨 **Load Model**
+To load the model from Hugging Face, use the following code:
+
+```python
+import keras
+from datetime import timedelta
+import numpy as np
+import pickle
+
+# Load the model from Hugging Face
+model = keras.saving.load_model("hf://VishSinh/cycle-sync")
+
+# Load the scalers (if needed)
+with open("feature_scaler.pkl", "rb") as f:
+    feature_scaler = pickle.load(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! 🚀😊