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app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+from scipy.sparse import load_npz
+import numpy as np
+
+# Load the saved model and a small subset of the data for the demo
+model = joblib.load("cell_classifier_model.joblib")
+X_data = load_npz("./data/X_data.npz")
+
+def predict_random_cell():
+    # Select a random cell from the dataset
+    random_index = np.random.randint(0, X_data.shape[0])
+    cell_data = X_data[random_index]
+
+    # Make a prediction
+    # The model expects a 2D array, so we reshape it
+    prediction = model.predict(cell_data.reshape(1, -1))[0]
+
+    return f"Selected a random cell from the dataset.\n\nModel Prediction: This cell belongs to Cluster {prediction}."
+
+# Create the Gradio web interface
+iface = gr.Interface(
+    fn=predict_random_cell,
+    inputs=None, # The user doesn't need to provide input
+    outputs=gr.Textbox(label="Prediction Result", lines=3),
+    title="CAR-T Cell Cluster Predictor",
+    description="Click the 'Submit' button to select a random cell from our dataset and see the model's prediction for which cluster it belongs to."
+)
+
+# Launch the app
+iface.launch()

cell_classifier_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:156808da11a0dea0574581291680d2555ccc9f19d44e08151874df60bf19a325
+size 63255577

requirements.txt

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+scikit-learn
+pandas
+numpy
+scipy
+joblib
+gradio