Create app.py

app.py

@@ -0,0 +1,33 @@
+import pickle
+import pandas as pd
+import gradio as gr
+
+path = "/content/drive/MyDrive/KSA/data/"
+
+data = pd.read_csv("Schedules_Train.csv")
+model = pickle.load(open("rf.pkl", "rb"))
+
+features = model[0].get_feature_names()
+features_cat = [m['col'] for m in model[0].mapping]
+
+def estimate_duration(*args):
+    print(args)
+    return model.predict(pd.DataFrame([args], columns=features)).round(2)
+
+with gr.Blocks(title="Remaining Duration Estimator", css="footer {visibility: hidden}") as demo:
+  inputs = []
+  for f in features:
+    if f in features_cat:
+      uniques = data[f].unique().tolist()
+      input = gr.Dropdown(uniques, value=uniques[0], label=f)
+    else:
+      input = gr.Slider(data[f].min(), data[f].max(), label=f)
+    inputs.append(input)
+
+  btn = gr.Button("Estimate Remaining Duration")
+  output = gr.Number(label="Estimated Remaining Duration")
+  btn.click(fn=estimate_duration, inputs=inputs, outputs=output)
+  gr.Examples(data[features].sample(n = 10).values.tolist(), fn=estimate_duration,
+              inputs=inputs, outputs=output, cache_examples=True)
+
+demo.launch(debug=False, show_api=False)