upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Untitled34.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/10XoO4F_D0n2dxdvDhQvdQWEzJKTEoQYF
+"""
+
+!pip install gradio
+
+import matplotlib.pyplot as plt
+import io
+from PIL import Image
+import pickle
+import pandas as pd
+import gradio as gr
+
+import gradio as gr
+import pandas as pd
+import joblib
+
+# Cargar el modelo de pronóstico
+model = joblib.load('/content/modelo_rf.pkl')
+
+# Definir las opciones para 'Borough' y 'Tipo_de_taxi'
+borough_options = ['Bronx', 'Brooklyn', 'Manhattan', 'Staten Island']
+taxi_options = ['yellow', 'green']
+
+# Función para realizar las predicciones
+def make_predictions(borough, taxi, years):
+    # Crear un DataFrame con las características de entrada
+    df = pd.DataFrame({'borough': [borough], 'Tipo_de_taxi': [taxi]})
+
+    # Generar los años de pronóstico
+    years_range = pd.date_range(start='today', periods=years, freq='Y').year
+
+    # Realizar las predicciones para cada año de pronóstico
+    predictions = []
+    for year in years_range:
+        df['Date'] = pd.to_datetime(year, format='%Y')
+        prediction = model.predict(df)
+        predictions.append(prediction)
+
+    # Crear un DataFrame con los años y las predicciones
+    result_df = pd.DataFrame({'Year': years_range, 'Prediction': predictions})
+
+    return result_df
+
+# Interfaz de Gradio
+iface = gr.Interface(
+    fn=make_predictions,
+    inputs=[
+        gr.inputs.Dropdown(choices=borough_options, label='Borough'),
+        gr.inputs.Dropdown(choices=taxi_options, label='Tipo_de_taxi'),
+        gr.inputs.Slider(minimum=1, maximum=10, default=5, label='Years')
+    ],
+    outputs=gr.outputs.Dataframe(headers=['Year', 'Prediction'])
+)
+
+# Ejecutar la interfaz de Gradio
+iface.launch()