Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from joblib import load
+from tensorflow.keras.models import load_model
+import tensorflow as tf
+import requests
+
+# Load models from Hugging Face (update with your model URLs)
+def load_huggingface_model(model_url):
+    response = requests.get(model_url)
+    with open("model_temp.joblib" if model_url.endswith(".joblib") else "model_temp.h5", "wb") as f:
+        f.write(response.content)
+
+    return load("model_temp.joblib") if model_url.endswith(".joblib") else load_model("model_temp.h5")
+
+# URLs of your models on Hugging Face
+MODEL_URLS = {
+    'Deep Learning': 'https://huggingface.co/your-username/best_DeepLearning_model/resolve/main/best_DeepLearning_model.h5',
+    'Support Vector Regression': 'https://huggingface.co/your-username/best_SVR_model/resolve/main/best_SVR_model.joblib',
+    'Random Forest Regression': 'https://huggingface.co/your-username/best_RandomForestRegressor_model/resolve/main/best_RandomForestRegressor_model.joblib',
+    'Decision Tree Regression': 'https://huggingface.co/your-username/best_DecisionTreeRegressor_model/resolve/main/best_DecisionTreeRegressor_model.joblib'
+}
+
+# Prediction function
+def multi_inputs(input1, input2, input3, input4, input5, input6, input7, input8, input9, input10, input11, input12):
+    try:
+        model = load_huggingface_model(MODEL_URLS[input12])
+
+        # Convert numeric inputs to float
+        input4, input5, input9 = float(input4), float(input5), float(input9)
+
+        # Create input list with proper types
+        inputs_to_transform = [
+            str(input1), str(input2), str(input3), input4, input5,
+            float(input6), float(input7), float(input8), input9,
+            str(input10), str(input11)
+        ]
+
+        # Reshape input for prediction
+        input_data = np.array([inputs_to_transform], dtype=np.float32)
+
+        if input12 == 'Deep Learning':
+            tensor_input = tf.convert_to_tensor(input_data)
+            prediction = model.predict(tensor_input, verbose=0)
+            return float(prediction[0][0])
+        else:
+            prediction = model.predict(input_data)
+            return float(prediction[0])
+
+    except Exception as e:
+        return f"Error in prediction: {str(e)}"
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=multi_inputs,
+    inputs=[
+        gr.Radio(['male', 'female'], label='Gender'),
+        gr.Dropdown(['Race1', 'Race2'], label='Race'),
+        gr.Dropdown([20, 30, 40], label='Age'),
+        gr.Number(label='Height (cm)'),
+        gr.Number(label='Weight (kg)'),
+        gr.Radio([0.0, 1.0], label='Diabetes'),
+        gr.Radio([0.0, 1.0], label='Simvastatin'),
+        gr.Radio([0.0, 1.0], label='Amiodarone'),
+        gr.Number(label='INR'),
+        gr.Dropdown(['Cyp2C9_A', 'Cyp2C9_B'], label='Cyp2C9 genotypes'),
+        gr.Dropdown(['VKORC1_A', 'VKORC1_B'], label='VKORC1 genotypes'),
+        gr.Dropdown(['Deep Learning', 'Support Vector Regression', 'Random Forest Regression', 'Decision Tree Regression'], label='Model Selection')
+    ],
+    outputs="number",
+    live=False
+)
+
+if __name__ == '__main__':
+    interface.launch(debug=True)