Upload 3 files

gr.File.py

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+outputs=[
+    gr.Textbox(label="Fraud Probability"),
+    gr.File(label="Transaction Network Graph (PNG)"),  # Use gr.File
+    gr.Textbox(label="Feature Importance Explanation"),
+]

pil_image

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+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Transaction Amount", minimum=0),
+        gr.Number(label="Time of Day (24h)", minimum=0, maximum=24),
+        gr.Textbox(label="User Role", placeholder="e.g., CFO, AP"),
+        gr.Number(label="Keystroke Speed (WPM)", minimum=0),
+        gr.Number(label="Typing Error Rate", minimum=0, maximum=1),
+    ],
+    outputs=[
+        gr.Textbox(label="Fraud Probability"),
+        gr.Image(label="Transaction Network Graph"),  # Accepts PIL Image
+        gr.Textbox(label="Feature Importance Explanation"),
+    ],
+    title="Fraud Detection with Behavioral Analytics",
+    description="Detects anomalies in transaction patterns using unsupervised learning and behavioral biometrics.",
+    examples=[
+        [5000, 23, "CFO", 25, 0.1],
+        [100, 9, "AP", 50, 0.01],
+        [20000, 20, "CFO", 20, 0.2]
+    ]
+)

pilot_graph

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+from PIL import Image
+import io
+import matplotlib.pyplot as plt
+
+def plot_graph(self, G):
+    plt.figure(figsize=(3, 2))
+    nx.draw(G, with_labels=True, node_color="lightblue", font_weight="bold")
+    plt.axis("off")
+    plt.tight_layout()
+
+    # Save the figure to a BytesIO buffer
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png")
+    plt.close()
+
+    # Load the image from the buffer into a PIL Image
+    image = Image.open(buf)
+    return image