Upload app.py

app.py

@@ -0,0 +1,31 @@
+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)