Create app.py

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+# 1. Download your model from your repo
+model_path = hf_hub_download(repo_id="Aaravkumar/heart-disease-prediction", filename="heart_disease_model.joblib")
+model = joblib.load(model_path)
+
+# 2. Define the prediction function
+def predict(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal):
+    # Create a dataframe matching your training columns
+    input_data = pd.DataFrame([[age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal]], 
+                              columns=['Age', 'Sex', 'Chest pain type', 'BP', 'Cholesterol', 'FBS over 120', 
+                                       'EKG results', 'Max HR', 'Exercise angina', 'ST depression', 
+                                       'Slope of ST', 'Number of vessels fluro', 'Thallium'])
+    
+    prediction = model.predict(input_data)[0]
+    probability = model.predict_proba(input_data)[0][1]
+    
+    result = "❤️ Heart Disease Detected" if prediction == 1 else "✅ No Heart Disease Detected"
+    return f"{result} (Confidence: {probability:.2%})"
+
+# 3. Create a beautiful UI
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Slider(20, 90, value=50, label="Age"),
+        gr.Radio([0, 1], label="Sex (1=M, 0=F)"),
+        gr.Slider(1, 4, step=1, label="Chest Pain Type"),
+        gr.Number(label="Resting BP (trestbps)"),
+        gr.Number(label="Cholesterol"),
+        gr.Radio([0, 1], label="Fasting Blood Sugar > 120 (1=True)"),
+        gr.Slider(0, 2, step=1, label="Resting EKG"),
+        gr.Number(label="Max Heart Rate"),
+        gr.Radio([0, 1], label="Exercise Induced Angina"),
+        gr.Number(label="ST Depression (oldpeak)"),
+        gr.Slider(1, 3, step=1, label="Slope of ST"),
+        gr.Slider(0, 3, step=1, label="Major Vessels (ca)"),
+        gr.Slider(1, 3, step=1, label="Thallium")
+    ],
+    outputs="text",
+    title="Heart Disease Risk Analyzer",
+    description="Enter patient vitals to predict cardiac risk using Aarav's Random Forest Model."
+)
+
+demo.launch()