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Update app.py
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app.py
CHANGED
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@@ -7,9 +7,9 @@ import zipfile
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import pathlib
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import shutil
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#
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# 1) Load
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#
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pkl_path = hf_hub_download(
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repo_id="cassieli226/hw2-airline-automl",
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filename="autogluon_predictor.pkl",
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@@ -19,9 +19,9 @@ pkl_path = hf_hub_download(
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with open(pkl_path, "rb") as f:
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predictor = cloudpickle.load(f)
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#
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# 2) load predictor
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#
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zip_path = hf_hub_download(
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repo_id="cassieli226/hw2-airline-automl",
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filename="autogluon_predictor_dir.zip",
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@@ -35,12 +35,11 @@ if extract_dir.exists():
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with zipfile.ZipFile(zip_path, "r") as zf:
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zf.extractall(str(extract_dir))
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# Load predictor directory (if needed)
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predictor_dir = ag.TabularPredictor.load(str(extract_dir))
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#
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# 3)
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#
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def predict_flight(stops, days_from_departure, flight_time, price, day_of_week, destination):
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X = pd.DataFrame({
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"Stops": [stops],
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@@ -52,9 +51,12 @@ def predict_flight(stops, days_from_departure, flight_time, price, day_of_week,
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})
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return predictor.predict(X)[0]
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with gr.Blocks() as demo:
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gr.Markdown("# Flight Duration Predictor")
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with gr.Row():
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with gr.Column():
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stops_in = gr.Slider("Stops", 0, 3, 1)
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@@ -74,7 +76,7 @@ with gr.Blocks() as demo:
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outputs=[output]
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)
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#
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#
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#
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demo.launch()
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import pathlib
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import shutil
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# -----------------------------
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# 1) Load pickled AutoGluon predictor
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# -----------------------------
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pkl_path = hf_hub_download(
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repo_id="cassieli226/hw2-airline-automl",
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filename="autogluon_predictor.pkl",
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with open(pkl_path, "rb") as f:
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predictor = cloudpickle.load(f)
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# -----------------------------
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# 2) load predictor directory for leaderboard
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# -----------------------------
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zip_path = hf_hub_download(
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repo_id="cassieli226/hw2-airline-automl",
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filename="autogluon_predictor_dir.zip",
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with zipfile.ZipFile(zip_path, "r") as zf:
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zf.extractall(str(extract_dir))
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predictor_dir = ag.TabularPredictor.load(str(extract_dir))
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# -----------------------------
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# 3) Gradio interface function
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# -----------------------------
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def predict_flight(stops, days_from_departure, flight_time, price, day_of_week, destination):
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X = pd.DataFrame({
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"Stops": [stops],
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})
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return predictor.predict(X)[0]
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# -----------------------------
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# 4) Gradio UI
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# -----------------------------
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with gr.Blocks() as demo:
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gr.Markdown("# Flight Duration Predictor")
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with gr.Row():
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with gr.Column():
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stops_in = gr.Slider("Stops", 0, 3, 1)
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outputs=[output]
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)
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# -----------------------------
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# 5) Launch
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# -----------------------------
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demo.launch()
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