Upload 3 files

app.py

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+# %%
+import gradio as gr
+import pandas as pd
+from sklearn.datasets import load_iris
+import pickle
+import os
+
+# Load model from file
+cwd = os.getcwd()
+print(cwd)
+model_filename = "iris_random_forest_classifier.pkl"
+with open(model_filename, mode="rb") as f:
+    model = pickle.load(f)
+# Load dataset
+iris = load_iris(as_frame=True)
+
+def predict(sepal_length, sepal_width, petal_length, petal_width):
+    input_data = pd.DataFrame([[sepal_length, sepal_width, petal_length, petal_width]],
+                              columns=iris.feature_names)
+    prediction = model.predict(input_data)[0]
+    return iris.target_names[prediction]
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Sepal Length"),
+        gr.Number(label="Sepal Width"),
+        gr.Number(label="Petal Length"),
+        gr.Number(label="Petal Width"),
+    ],
+    outputs="text",
+    examples=[
+        [5.1, 3.5, 1.4, 0.2],
+        [6.2, 2.9, 4.3, 1.3],
+        [7.7, 3.8, 6.7, 2.2],
+    ],
+    title="Iris Flower Prediction",
+    description="Enter the sepal and petal measurements to predict the Iris species."
+)
+
+demo.launch()

requirements.txt

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+scikit-learn