Create app.py

app.py

@@ -0,0 +1,78 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+
+
+def run_model(file):
+    if file is None:
+        return "Please upload a file", "Please upload a file", "Please upload a file"
+
+    # Placeholder prediction logic
+    max_credit_limit = 87500
+    mean_credit_limit = 42300
+    min_credit_limit = 15000
+
+    return (
+        f"${max_credit_limit:,.0f}",
+        f"${mean_credit_limit:,.0f}",
+        f"${min_credit_limit:,.0f}",
+    )
+
+
+def load_fake_data():
+    rng = np.random.default_rng(42)
+    n = 30
+
+    customer_names = [f"Customer {i+1}" for i in range(n)]
+    tax_ids = [f"{rng.integers(10, 99)}.{rng.integers(100, 999)}.{rng.integers(100, 999)}/0001-{rng.integers(10, 99)}" for _ in range(n)]
+    mcust_no = [f"MC{rng.integers(100000, 999999)}" for _ in range(n)]
+
+    credit_limit = rng.integers(5000, 95000, n)
+    max_credit_limit = credit_limit + rng.integers(1000, 15000, n)
+    min_credit_limit = credit_limit - rng.integers(1000, 10000, n)
+    min_credit_limit = np.clip(min_credit_limit, 0, None)
+
+    return pd.DataFrame({
+        "customer_name": customer_names,
+        "tax_id": tax_ids,
+        "mcust_no": mcust_no,
+        "credit_limit": credit_limit,
+        "max_credit_limit": max_credit_limit,
+        "min_credit_limit": min_credit_limit,
+    })
+
+
+with gr.Blocks(title="Credit Limit Model") as demo:
+    # ---------- Header ----------
+    gr.Markdown("# Credit Limit Model")
+    gr.Markdown("AI-based credit limit model to automatically predict credit-lines.")
+    gr.Markdown("⚠️ **The current model is limited to new small clients (with expected credit lines below 100K).**")
+
+    gr.Markdown("---")
+
+    # ---------- Section 1: New prediction ----------
+    gr.Markdown("## Make a new prediction")
+    gr.Markdown("Upload your excel template to have a company analysed")
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Prediction Results")
+            max_out = gr.Textbox(label="Max Credit Limit", value="$XXX", interactive=False)
+            mean_out = gr.Textbox(label="Mean Credit Limit", value="$XXX", interactive=False)
+            min_out = gr.Textbox(label="Min Credit Limit", value="$XXX", interactive=False)
+
+        with gr.Column():
+            file_input = gr.File(label="Upload Excel template", file_types=[".xlsx", ".xls", ".xlsm"])
+            run_button = gr.Button("Run Model")
+
+    run_button.click(fn=run_model, inputs=file_input, outputs=[max_out, mean_out, min_out])
+
+    gr.Markdown("---")
+
+    # ---------- Section 2: Past predictions ----------
+    gr.Markdown("## Visualize past predictions")
+    gr.Dataframe(value=load_fake_data(), interactive=False, wrap=True)
+
+
+if __name__ == "__main__":
+    demo.launch()