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)