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IrisModel.py

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+
+import gradio as gr
+import pickle
+import numpy as np
+
+# Load the trained model
+with open('model.pkl', 'rb') as f:
+    model = pickle.load(f)
+
+def predict(sepal_length, sepal_width, petal_length, petal_width):
+    input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+    prediction = model.predict(input_data)
+    return prediction[0]
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=["number", "number", "number", "number"],
+    outputs="text",
+    title="Iris Flower Classifier",
+    description="Enter the features of the iris flower to predict its species."
+)
+
+interface.launch()