feat: Initialize Gradio interface with dataset selection, prediction, and authentication

README.md

@@ -1,6 +1,6 @@
 ---
 title: Demographic 33k Alpha Ui
-emoji: 👀
+emoji: 🤖
 colorFrom: pink
 colorTo: indigo
 sdk: gradio

app.py

@@ -0,0 +1,157 @@
+import gradio as gr
+import pandas as pd
+
+available_datasets = [
+    "Demographic",
+    "Health",
+    "Financial",
+    "Social",
+    "Economic",
+    "Political",
+]
+
+default_dataset = "Demographic"
+
+available_attributes = [
+    "Income",
+    "Age",
+    "Marital Status",
+    "Education",
+]
+
+default_attributes = ["Income", "Age"]
+
+
+def load_dataset(dataset):
+    """
+    Load and return data based on the 'dataset' parameter.
+
+    Parameters:
+    - dataset (str): The name of the dataset to load. Currently, this parameter is not used as the function returns a static DataFrame.
+
+    Returns:
+    - pd.DataFrame: A pandas DataFrame containing the loaded data.
+    """
+    return pd.DataFrame(
+        {
+            "Name": ["John", "Doe", "Jane", "Smith"],
+            "Age": [25, 30, 35, 40],
+            "Income": [50000, 60000, 70000, 80000],
+            "Marital Status": ["Single", "Married", "Single", "Married"],
+            "Education": ["High School", "Bachelor", "Master", "PhD"],
+        }
+    )
+
+
+def predict(dataset, attributes, access_token):
+    """
+    Simulates predictions based on the provided dataset and attributes. Requires an access token for authentication.
+
+    Parameters:
+    - dataset (str): The name of the dataset on which to base predictions. This parameter is currently not used as the function returns a static prediction.
+    - attributes (list of str): The attributes selected for prediction. This parameter is currently not used as the function returns a static prediction.
+    - access_token (str): The access token required for authentication to make predictions.
+
+    Returns:
+    - tuple: A tuple containing a prediction message (str) and a pandas DataFrame with prediction results.
+
+    Raises:
+    - gr.Error: If the access token is not provided, an error is raised indicating that an access token is required.
+    """
+    if not access_token:
+        raise gr.Error(
+            "Access token missing or invalid. Please ensure you have a valid access token."
+        )
+
+    prediction_message = "200 predictions made in 2.5 seconds."
+    prediction_results = pd.DataFrame(
+        {
+            "Name": ["John", "Doe", "Jane", "Smith"],
+            "Predicted Value": [25000, 30000, 35000, 40000],
+        }
+    )
+    return prediction_message, prediction_results
+
+
+def load_dataset_and_predict(dataset, attributes, access_token):
+    """
+    Combines data loading and prediction into a single step, requiring an access token for the prediction part. Uses the 'load_dataset' and 'predict' functions to load the data and generate predictions.
+
+    Parameters:
+    - dataset (str): The name of the dataset to load and predict on.
+    - attributes (list of str): The attributes selected for making predictions.
+    - access_token (str): The access token required for authentication to make predictions.
+
+    Returns:
+    - tuple: A tuple containing the loaded pandas DataFrame, a prediction message (str), and a pandas DataFrame with prediction results.
+
+    Note:
+    - If the access token is not provided, the prediction part is skipped, and the prediction message and results will be returned as empty or None.
+    """
+    loaded_data = load_dataset(dataset)
+
+    if access_token:
+        prediction_message, prediction_results = predict(
+            dataset, attributes, access_token
+        )
+    else:
+        prediction_message = "No access token provided, prediction skipped."
+        prediction_results = None
+
+    return loaded_data, prediction_message, prediction_results
+
+
+interface_theme = gr.themes.Default()
+
+with gr.Blocks(theme=interface_theme) as demo:
+    gr.Markdown("### Authenticate")
+    access_token = gr.Textbox(
+        type="password",
+        label="Access Token",
+        placeholder="Enter your access token here.",
+    )
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Select Dataset and Attributes")
+
+            selected_dataset = gr.Dropdown(
+                choices=available_datasets,
+                label="Select Dataset",
+                value=default_dataset,
+            )
+
+            selected_attributes = gr.Dropdown(
+                choices=available_attributes,
+                label="Select Attributes",
+                info="You can select multiple attributes.",
+                multiselect=True,
+                value=default_attributes,
+            )
+
+    gr.Markdown("### Dataset Preview")
+    dataset_preview = gr.Dataframe()
+
+    predict_button = gr.Button("Predict Attributes")
+
+    gr.Markdown("### Prediction Results")
+    prediction_preview = gr.Dataframe()
+    prediction_label = gr.Markdown("")
+
+    selected_dataset.change(
+        fn=load_dataset, inputs=selected_dataset, outputs=dataset_preview
+    )
+
+    predict_button.click(
+        fn=predict,
+        inputs=[selected_dataset, selected_attributes, access_token],
+        outputs=[prediction_label, prediction_preview],
+    )
+
+    demo.load(
+        fn=load_dataset_and_predict,
+        inputs=[selected_dataset, selected_attributes, access_token],
+        outputs=[dataset_preview, prediction_label, prediction_preview],
+    )
+
+demo.launch()

requirements.txt

@@ -0,0 +1,2 @@
+requests==2.31.0
+pandas==2.2.1