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main.py

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+import gradio as gr
+import pickle
+
+def model(sl,sw,pl,pw):
+  sepal_length = float()
+  sepal_width = float()
+  petal_length = float()
+  petal_width = float()
+  dataframe = pd.DataFrame({"sepal length (cm)":[sepal_length],"sepal width (cm)":[sepal_width],'petal length (cm)':[petal_length],'petal width (cm)':[petal_width]})
+  with open('/content/model.pkl', 'rb') as file:
+    loaded_model = pickle.load(file)
+    output = loaded_model.predict(dataframe)
+    if output == 0:
+        return"The output class is setosa"
+    elif output == 1:
+        return"The output class is versicolor"
+    elif output == 2:
+        return"The output class is virginica"
+
+with gr.Blocks() as demo:
+  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)
+    petal_length = gr.Number(label="Petal length (cm)", value=1.1)
+    petal_width = gr.Number(label="Petal width (cm)", value=2.1)
+  with gr.Row():
+    outputs = gr.Textbox(label='Prediction')
+    run = gr.Button(value="Prediction")
+
+    run.click(model, inputs=[sepal_length, sepal_width, petal_length, petal_width], outputs=outputs)
+demo.launch(debug=True, share=True)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7b5bafc94b2a5cce583143571238404c3332de71d8b1e4dc20ef8ad6f4e103f4
+size 993