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app.py

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+import gradio as gr
+import pandas as pd
+import pickle
+import os
+
+# Define params names
+PARAMS_NAME = [
+            "gender",
+            "age",
+            "hypertension",
+            "heart_disease",
+            "ever_married",
+            "work_type",
+            "Residence_type",
+            "avg_glucose_level",
+            "bmi",
+            "smoking_status"
+]
+
+           
+# Load model
+with open("model/model1.pkl", "rb") as f:
+    model = pickle.load(f)
+
+
+import os
+
+# Hacking my own protocol
+os.chmod('model/saved_bins_bmi.pkl', 0o777)
+
+with open('model/saved_bins_bmi.pkl', 'rb') as handle:
+    saved_bins_bmi = pickle.load(handle)
+
+
+def predict(*args):
+    answer_dict = {}
+
+    for i in range(len(PARAMS_NAME)):
+        answer_dict[PARAMS_NAME[i]] = [args[i]]
+
+    # Crear dataframe
+    single_instance = pd.DataFrame.from_dict(answer_dict)
+
+
+    single_instance["bmi"] = pd.cut(single_instance['bmi'],
+                                     bins=saved_bins_bmi, 
+                                     include_lowest=True)
+    single_instance['bmi'] = single_instance['bmi'].cat.add_categories('null')
+
+    single_instance_numbers = single_instance
+    
+    for columna in single_instance_numbers:
+            # Verificar si el tipo de dato es "object"
+            if single_instance_numbers[columna].dtype == 'object':
+                # Obtener los valores únicos de la columna
+                valores_unicos = single_instance_numbers[columna].unique()
+                
+                # Crear un diccionario de reemplazo
+                diccionario_reemplazo = {valor: indice for indice, valor in enumerate(valores_unicos)}
+                
+                # Reemplazar los valores en la columna
+                single_instance_numbers[columna] = single_instance_numbers[columna].map(diccionario_reemplazo)
+
+    dataEnd_ohe = pd.get_dummies(single_instance_numbers).fillna(0)
+
+    
+    prediction = model.predict(dataEnd_ohe)
+
+
+    # Cast numpy.int64 to just a int
+    stroke = int(prediction[0])
+
+
+    # Adaptación respuesta
+    response = stroke
+    if stroke == 1:
+        response = "Keep rockin' babe!"
+    if stroke == 0:
+        response = "This brain will colapse in 3.. 2.. 1.. 🤯 "
+
+
+    return response
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        #   Stroke Prevention 🤯
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+
+            gr.Markdown(
+                """
+                ## Insert your self data here please 🤓
+                """
+            )
+            
+            gender = gr.Radio(
+                label='Gender',
+                choices=['Male', 'Female'],
+                value='Female',
+            )
+
+            age = gr.Slider(
+                label='Age',
+                minimum=35.0,
+                maximum=82.0,
+                step=1,
+                randomize=True
+            )
+
+            hypertension = gr.Radio(
+                label='Hypertension',
+                choices=['No', 'Yes'],
+                value='No',
+            )
+
+            heart_disease = gr.Radio(
+                label='Heart Disease',
+                choices=['Yes', 'No'],
+                value='No',
+            )
+
+            ever_married = gr.Radio(
+                label='Ever Married',
+                choices=['Yes', 'No'],
+                value='Yes',
+            )
+
+            work_type = gr.Radio(
+                label='Work Type',
+                choices=['Private', 'Self-employed', 'Govt-job'],
+                value='Private',
+            )
+
+            Residence_type = gr.Radio(
+                label='Residence Type',
+                choices=['Urban', 'Rural'],
+                value='Urban',
+            )
+
+            avg_glucose_level = gr.Slider(
+                label='Avg Glucose Level',
+                minimum=55.22,
+                maximum=271.74,
+                step=0.1,
+                randomize=True
+            )
+
+            bmi = gr.Slider(
+                label='Bmi',
+                minimum=11.3,
+                maximum=92.0,
+                step=0.1,
+                randomize=True
+            )
+
+            smoking_status = gr.Dropdown(
+                label='Smoking Status',
+                choices=['formerly smoked', 'never smoked', 'smokes', 'Unknown'],
+                multiselect=False,
+                value='never smoked',
+            )         
+
+
+
+
+        with gr.Column():
+
+            gr.Markdown(
+                """
+                ## Look if your brain is in risk 🧠
+                """
+            )
+
+            label = gr.Label(label="Brain status")
+            predict_btn = gr.Button(value="Click me please!")
+            predict_btn.click(
+                predict,
+                inputs=[
+                    gender,
+                    age,
+                    hypertension,
+                    heart_disease,
+                    ever_married,
+                    work_type,
+                    Residence_type,
+                    avg_glucose_level,
+                    bmi,
+                    smoking_status,
+                ],
+                outputs=[label],
+                api_name="prediccion"
+            )
+            
+            gr.Markdown(
+                """
+                ## <img src="https://media.giphy.com/media/ijb5ZE9zIQ2Nq/giphy.gif" alt="GIF">
+                """
+            )
+
+    gr.Markdown(
+        """
+        <p style='text-align: center'>
+            <a href='https://www.escueladedatosvivos.ai/cursos/bootcamp-de-data-science' 
+                target='_blank'>Proyecto demo creado en el bootcamp de EDVAI 🤗
+            </a>
+        </p>
+        <p style='text-align: center'>
+            <a href='https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset' 
+                target='_blank'>Data From IStroke Prediction Dataset update by Fede Soriano
+            </a>
+        </p>
+        """
+    )
+
+demo.launch()