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README.md

@@ -1,12 +1,18 @@
 ---
-title: Iris Rf Gradio Space
-emoji: 👁
-colorFrom: red
-colorTo: pink
+title: Iris RF Gradio Space
+emoji: 🌸
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 6.10.0
+python_version: 3.12
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Iris RF Gradio Space
+
+Esta Space muestra una interfaz sencilla para probar el modelo publicado en:
+
+- Modelo: `DilanMP/iris-rf-joblib-demo`
+
+La app descarga `model.joblib` directamente desde el Hub.

app.py

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+import joblib
+import pandas as pd
+import gradio as gr
+from huggingface_hub import hf_hub_download
+
+MODEL_REPO_ID = "DilanMP/iris-rf-joblib-demo"
+TARGET_LABELS = [np.str_('setosa'), np.str_('versicolor'), np.str_('virginica')]
+
+model_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename="model.joblib")
+model = joblib.load(model_path)
+
+def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
+    row = pd.DataFrame([{
+        "sepal length (cm)": sepal_length,
+        "sepal width (cm)": sepal_width,
+        "petal length (cm)": petal_length,
+        "petal width (cm)": petal_width
+    }])
+
+    pred = model.predict(row)[0]
+    return TARGET_LABELS[pred]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Clasificador de Iris")
+    gr.Markdown(
+        "Esta Space usa un modelo publicado en Hugging Face Hub y permite probarlo con una interfaz sencilla."
+    )
+
+    with gr.Row():
+        sepal_length = gr.Number(label="Sepal length (cm)", value=5.1)
+        sepal_width = gr.Number(label="Sepal width (cm)", value=3.5)
+
+    with gr.Row():
+        petal_length = gr.Number(label="Petal length (cm)", value=1.4)
+        petal_width = gr.Number(label="Petal width (cm)", value=0.2)
+
+    btn = gr.Button("Predecir")
+    output = gr.Textbox(label="Clase predicha")
+
+    btn.click(
+        fn=predict_iris,
+        inputs=[sepal_length, sepal_width, petal_length, petal_width],
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+pandas
+joblib
+scikit-learn
+huggingface_hub