Delete app.py

app.py

@@ -1,75 +0,0 @@
-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)