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Prueba_1_con_RF.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2zN17g8q2JSN",
+        "outputId": "1eed363e-9824-4f06-9f85-a9d5d65a2f3d"
+      },
+      "outputs": [
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+          "name": "stdout",
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+          ]
+        }
+      ],
+      "source": [
+        "# Instalar librerías necesarias\n",
+        "!pip install gradio scikit-learn openpyxl pandas\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import gradio as gr\n",
+        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "from sklearn.metrics import mean_squared_error"
+      ],
+      "metadata": {
+        "id": "EI5LyrH62Oi9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "fDxWOYB7f8Ei"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 🔹 Función que encapsula TODO tu flujo\n",
+        "def ejecutar_modelo():\n",
+        "    # 1. Cargar datos (simulamos con dataset de sklearn o podrías leer desde tu Drive)\n",
+        "    from sklearn.datasets import load_diabetes\n",
+        "    data = load_diabetes(as_frame=True)\n",
+        "    X, y = data.data, data.target\n",
+        "\n",
+        "    # 2. Split train/test\n",
+        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "\n",
+        "    # 3. Entrenar modelo\n",
+        "    model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
+        "    model.fit(X_train, y_train)\n",
+        "\n",
+        "    # 4. Predicciones\n",
+        "    y_pred = model.predict(X_test)\n",
+        "\n",
+        "    # 5. Guardar resultados en Excel\n",
+        "    df_resultados = pd.DataFrame({\n",
+        "        \"Real\": y_test.values,\n",
+        "        \"Predicho\": y_pred\n",
+        "    })\n",
+        "    output_path = \"resultados.xlsx\"\n",
+        "    df_resultados.to_excel(output_path, index=False)\n",
+        "\n",
+        "    return output_path\n"
+      ],
+      "metadata": {
+        "id": "m9nt4GOL2Kln"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Interfaz: un botón para correr el modelo y un botón para descargar Excel\n",
+        "with gr.Blocks() as demo:\n",
+        "    gr.Markdown(\"## Random Forest Predictor 📊\")\n",
+        "    gr.Markdown(\"Haz clic en el botón para entrenar el modelo y obtener resultados\")\n",
+        "\n",
+        "    boton = gr.Button(\"Ejecutar modelo\")\n",
+        "    salida = gr.File(label=\"Descargar Excel de resultados\")\n",
+        "\n",
+        "    boton.click(fn=ejecutar_modelo, inputs=None, outputs=salida)\n",
+        "\n",
+        "demo.launch()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 646
+        },
+        "id": "R9PNIOdu2zTd",
+        "outputId": "fb32c8a1-74dd-4607-b5eb-546b13a86d18"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "It looks like you are running Gradio on a hosted Jupyter notebook, which requires `share=True`. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
+            "\n",
+            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
+            "* Running on public URL: https://8ee144fa096d18fed6.gradio.live\n",
+            "\n",
+            "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<div><iframe src=\"https://8ee144fa096d18fed6.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": []
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    }
+  ]
+}

app.py

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+# -*- coding: utf-8 -*-
+"""Prueba 1 con RF.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1uLC6Z9l_iiLNQSpLQ8srEj6ziSJ6sPxs
+"""
+
+# Instalar librerías necesarias
+!pip install gradio scikit-learn openpyxl pandas
+
+import gradio as gr
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error
+
+
+
+# 🔹 Función que encapsula TODO tu flujo
+def ejecutar_modelo():
+    # 1. Cargar datos (simulamos con dataset de sklearn o podrías leer desde tu Drive)
+    from sklearn.datasets import load_diabetes
+    data = load_diabetes(as_frame=True)
+    X, y = data.data, data.target
+
+    # 2. Split train/test
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # 3. Entrenar modelo
+    model = RandomForestRegressor(n_estimators=100, random_state=42)
+    model.fit(X_train, y_train)
+
+    # 4. Predicciones
+    y_pred = model.predict(X_test)
+
+    # 5. Guardar resultados en Excel
+    df_resultados = pd.DataFrame({
+        "Real": y_test.values,
+        "Predicho": y_pred
+    })
+    output_path = "resultados.xlsx"
+    df_resultados.to_excel(output_path, index=False)
+
+    return output_path
+
+# Interfaz: un botón para correr el modelo y un botón para descargar Excel
+with gr.Blocks() as demo:
+    gr.Markdown("## Random Forest Predictor 📊")
+    gr.Markdown("Haz clic en el botón para entrenar el modelo y obtener resultados")
+
+    boton = gr.Button("Ejecutar modelo")
+    salida = gr.File(label="Descargar Excel de resultados")
+
+    boton.click(fn=ejecutar_modelo, inputs=None, outputs=salida)
+
+demo.launch()