Create app.py

app.py

@@ -0,0 +1,26 @@
+import numpy as np
+import gradio as gr
+from sklearn.tree import DecisionTreeRegressor
+import pickle
+# from joblib import load
+
+tree_model = pickle.load(open('/content/weld-depth-and-width-flask/tree_model.pkl', 'rb'))
+# tree_model = load('tree_model.joblib')
+
+
+def predict(IW, IF, VW, FP):
+    params = [[float(IW), float(IF), float(VW), float(FP)]]
+    prediction = np.round(tree_model.predict(params), 2)
+    depth, width = prediction[0]
+    print(depth, width)
+    return depth, width
+
+input1 = gr.inputs.Slider(minimum=0, maximum=100, label="Input 1")
+input2 = gr.inputs.Slider(minimum=0, maximum=100, label="Input 2")
+input3 = gr.inputs.Slider(minimum=0, maximum=100, label="Input 3")
+input4 = gr.inputs.Slider(minimum=0, maximum=100, label="Input 4")
+
+output1 = gr.outputs.Textbox(label="Output")
+output2 = gr.outputs.Textbox(label="Output")
+interface = gr.Interface(fn=predict, inputs=[input1, input2], outputs=[output1, output2])
+interface.launch(debug=True, share=True)