delete useless files

README.md

@@ -1,12 +0,0 @@
----
-title: Diabetes Prediction
-emoji: 💻
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 4.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

test.py

@@ -1,48 +0,0 @@
-import gradio as gr
-import matplotlib.pyplot as plt
-import shap
-import hopsworks
-import pandas as pd
-import joblib
-
-# Assuming you have your model and data defined elsewhere
-project = hopsworks.login(
-    project="SonyaStern_Lab1",
-    api_key_value="c9StuuVQPoMUeXWe.jB2XeWcI8poKUN59W13MxAbMemzY7SChOnX151GtTFNhysBBUPMRuEp5IK7SE3i1",
-)
-mr = project.get_model_registry()
-model = mr.get_model("diabetes_model", version=1)
-model_dir = model.download()
-model = joblib.load(model_dir + "/diabetes_model.pkl")
-rf_model = model.steps[-1][1]  # Load your model
-df = pd.DataFrame(
-    [[20, 20, 30, 40]],
-    columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
-)
-
-
-def generate_plots():
-    # Create the first plot as before
-    fig1, ax1 = plt.subplots()
-    ax1.plot([1, 2, 3], [4, 5, 6])
-    ax1.set_title("Plot 1")
-
-    # Generate the SHAP waterfall plot for fig2
-    fig2 = shap.plots.waterfall(
-        shap.Explanation(
-            values=shap.Explainer(rf_model).shap_values(df)[1][0],
-            base_values=shap.Explainer(rf_model).expected_value[1],
-        )
-    )
-
-    return fig1, fig2
-
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        gr.Plot(generate_plots()[0])  # Display first plot in the first row
-
-    with gr.Row():
-        gr.Plot(generate_plots()[1])  # Display SHAP waterfall plot in the second row
-
-demo.launch()

testchat.py

@@ -1,50 +0,0 @@
-import gradio as gr
-import matplotlib.pyplot as plt
-import shap
-import hopsworks
-import pandas as pd
-import joblib
-
-project = hopsworks.login(
-    project="SonyaStern_Lab1",
-    api_key_value="c9StuuVQPoMUeXWe.jB2XeWcI8poKUN59W13MxAbMemzY7SChOnX151GtTFNhysBBUPMRuEp5IK7SE3i1",
-)
-mr = project.get_model_registry()
-model = mr.get_model("diabetes_model", version=1)
-model_dir = model.download()
-model = joblib.load(model_dir + "/diabetes_model.pkl")
-rf_model = model.steps[-1][1]  # Load your model
-df = pd.DataFrame(
-    [[20, 20, 30, 40]],
-    columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
-)
-
-
-def generate_plots():
-    # Create the first plot as before
-    fig1, ax1 = plt.subplots()
-    ax1.plot([1, 2, 3], [4, 5, 6])
-    ax1.set_title("Plot 1")
-
-    # Generate the SHAP waterfall plot for fig2
-    explainer = shap.Explainer(rf_model)
-    shap_values = explainer.shap_values(df)[1]  # Select SHAP values for class 1
-    shap_values_exp = shap.Explanation(
-        values=shap_values[0], base_values=explainer.expected_value[1]
-    )
-    ax2 = shap.plots.waterfall(
-        shap_values_exp, show=False
-    )  # Get the axis for the waterfall plot
-
-    return fig1, ax2
-
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        gr.Plot(generate_plots()[0])  # Display first plot in the first row
-
-    with gr.Row():
-        _, ax2 = generate_plots()
-        gr.Plot(ax2)  # Display SHAP waterfall plot in the second row
-
-demo.launch()

testcorcel.py

@@ -1,79 +0,0 @@
-import gradio as gr
-import matplotlib.pyplot as plt
-import shap
-import hopsworks
-import pandas as pd
-import joblib
-from sklearn.pipeline import make_pipeline
-
-df = pd.DataFrame(
-    [[20, 20, 30, 40]],
-    columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
-)
-
-
-# Assuming the hopsworks login and model retrieval code works as expected
-project = hopsworks.login(
-    project="SonyaStern_Lab1",
-    api_key_value="c9StuuVQPoMUeXWe.jB2XeWcI8poKUN59W13MxAbMemzY7SChOnX151GtTFNhysBBUPMRuEp5IK7SE3i1",
-)
-mr = project.get_model_registry()
-model = mr.get_model("diabetes_model", version=1)
-model_dir = model.download()
-model = joblib.load(model_dir + "/diabetes_model.pkl")
-print("printing model pipeline:", model)
-
-rf_classifier = model.named_steps["randomforestclassifier"]
-
-transformer_pipeline = make_pipeline(
-    *[
-        step
-        for name, step in model.named_steps.items()
-        if name != "randomforestclassifier"
-    ]
-)
-
-transformed_df = transformer_pipeline.transform(df)
-
-
-# rf_model = model.steps[-1][1]  # Load your model
-
-
-def generate_plots():
-    # Create the first plot as before
-    fig1, ax1 = plt.subplots()
-    ax1.plot([1, 2, 3], [4, 5, 6])
-    ax1.set_title("Plot 1")
-
-    # Generate the SHAP waterfall plot for fig2
-    explainer = shap.TreeExplainer(rf_classifier)
-
-    shap_values = explainer.shap_values(transformed_df)
-    predicted_class = rf_classifier.predict(transformed_df)[0]
-    shap_values_for_predicted_class = shap_values[predicted_class]
-    # base_value = explainer.expected_value[1]
-
-    fig2 = plt.figure()  # Create a new figure for SHAP plot
-    shap_explanation = shap.Explanation(
-        values=shap_values_for_predicted_class[0],
-        base_values=explainer.expected_value[predicted_class],
-        data=transformed_df[0],
-        feature_names=df.columns.tolist(),
-    )
-    shap.waterfall_plot(shap_explanation)
-    plt.title("SHAP Waterfall Plot")  # Optionally set a title for the SHAP plot
-
-    return fig1, fig2
-
-
-# Generate plots once and store them
-fig1, fig2 = generate_plots()
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        gr.Plot(fig1)  # Display first plot in the first row
-
-    with gr.Row():
-        gr.Plot(fig2)  # Display SHAP waterfall plot in the second row
-
-demo.launch()

testcorcel2.py

@@ -1,75 +0,0 @@
-import gradio as gr
-import matplotlib.pyplot as plt
-import shap
-import hopsworks
-import pandas as pd
-import joblib
-from sklearn.pipeline import make_pipeline
-
-df = pd.DataFrame(
-    [[20, 20, 30, 40]],
-    columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
-)
-
-
-# Assuming the hopsworks login and model retrieval code works as expected
-project = hopsworks.login(
-    project="SonyaStern_Lab1",
-    api_key_value="c9StuuVQPoMUeXWe.jB2XeWcI8poKUN59W13MxAbMemzY7SChOnX151GtTFNhysBBUPMRuEp5IK7SE3i1",
-)
-mr = project.get_model_registry()
-model = mr.get_model("diabetes_gan_model", version=1)
-model_dir = model.download()
-model = joblib.load(model_dir + "/diabetes_gan_model.pkl")
-print("printing model pipeline:", model)
-
-rf_classifier = model.named_steps["randomforestclassifier"]
-
-transformer_pipeline = make_pipeline(
-    *[
-        step
-        for name, step in model.named_steps.items()
-        if name != "randomforestclassifier"
-    ]
-)
-
-transformed_df = transformer_pipeline.transform(df)
-
-
-# rf_model = model.steps[-1][1]  # Load your model
-
-
-def generate_plots():
-    # Create the first plot as before
-    fig1, ax1 = plt.subplots()
-    ax1.plot([1, 2, 3], [4, 5, 6])
-    ax1.set_title("Plot 1")
-
-    # Generate the SHAP waterfall plot for fig2
-    explainer = shap.TreeExplainer(rf_classifier)
-
-    shap_values = explainer.shap_values(transformed_df)
-    predicted_class = rf_classifier.predict(transformed_df)[0]
-    shap_values_for_predicted_class = shap_values[predicted_class]
-    # base_value = explainer.expected_value[1]
-
-    fig2 = plt.figure()  # Create a new figure for SHAP plot
-    shap.waterfall_plot(
-        explainer.expected_value[predicted_class], shap_values_for_predicted_class[0]
-    )
-    plt.title("SHAP Waterfall Plot")  # Optionally set a title for the SHAP plot
-
-    return fig1, fig2
-
-
-# Generate plots once and store them
-fig1, fig2 = generate_plots()
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        gr.Plot(fig1)  # Display first plot in the first row
-
-    with gr.Row():
-        gr.Plot(fig2)  # Display SHAP waterfall plot in the second row
-
-demo.launch()