Update README.md

README.md

@@ -1,3 +1,4 @@
+---
 title: Insulin Dependency Predictor
 emoji: 💉
 colorFrom: indigo
@@ -6,41 +7,5 @@ sdk: gradio
 sdk_version: 5.30.0
 app_file: app.py
 pinned: true
-short_description: Predict insulin dependency in diabetic patients using clinical data.
-
-
-# 🧠 Insulin Dependency Predictor
-
-This interactive tool predicts whether a diabetic patient is likely to require **insulin therapy** based on their clinical information. The prediction model is trained using a **Random Forest classifier**, and the interface is built with **Gradio**.
-
-# 🩺 Features
-
-- Input patient data (e.g., age, BMI, HbA1c, glucose levels)
-- Real-time prediction of insulin dependency (Yes / No)
-- Built using Scikit-learn and hosted on Hugging Face Spaces
-- Experimental integration with ensemble models (LightGBM, XGBoost)
-
-# 🚀 Usage
-
-Enter the patient details and click **Submit**. The model will return a prediction for insulin dependency.
-
-# 📁 Model
-
-The model was trained on a real-world diabetes dataset with features such as:
-
-- Age, Gender, Height, Weight, BMI  
-- Fasting Blood Sugar (FBS), Postprandial Blood Sugar (PPBS)  
-- HbA1c, Smoking and Alcohol use, Diabetes duration  
-
-Model type: RandomForestClassifier  
-Saved as: `random_forest_model.pkl`
-
-# 📊 Future Work
-
-- Deploying ensemble models (Random Forest + LightGBM)
-- More feature engineering and model optimization
-- Clinical testing and validation
-
-# ⚠️ Disclaimer
-
-This is an **experimental tool** and should not be used for medical diagnosis. Always consult a licensed healthcare provider for medical advice.
+short_description: "Predict insulin need in diabetic patients."
+---