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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import joblib | |
| import xgboost as xgb | |
| from tensorflow.keras.models import load_model | |
| from sklearn.preprocessing import MinMaxScaler | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| # Load models & scalers | |
| xgb_clf = xgb.XGBClassifier() | |
| xgb_clf.load_model("xgb_model.json") | |
| xgb_reg = joblib.load("xgb_pipeline_model.pkl") | |
| scaler_X = joblib.load("scaler_X.pkl") | |
| scaler_y = joblib.load("scaler_y.pkl") | |
| lstm_model = load_model("lstm_revenue_model.keras") | |
| # Prediction + Plot functions | |
| def classify_fn(df: pd.DataFrame): | |
| preds = xgb_clf.predict(df) | |
| probs = xgb_clf.predict_proba(df) | |
| fig, ax = plt.subplots() | |
| ax.bar(['No Bankruptcy', 'Bankruptcy'], probs[0], color=['#4CAF50', '#F44336']) | |
| ax.set_ylim(0, 1) | |
| ax.set_title('Bankruptcy Probability') | |
| ax.set_ylabel('Probability') | |
| plt.tight_layout() | |
| return {"Predicted Label": int(preds[0])}, fig | |
| def regress_fn(df: pd.DataFrame): | |
| preds = xgb_reg.predict(df) | |
| fig, ax = plt.subplots() | |
| sns.histplot(preds, bins=20, kde=True, ax=ax) | |
| ax.set_title('Anomaly Score Distribution') | |
| ax.set_xlabel('Predicted Anomaly Score') | |
| plt.tight_layout() | |
| return preds.tolist(), fig | |
| def lstm_fn(seq_str: str): | |
| vals = np.array(list(map(float, seq_str.split(',')))).reshape(1, -1) | |
| vals_s = scaler_X.transform(vals).reshape((1, vals.shape[1], 1)) | |
| pred_s = lstm_model.predict(vals_s) | |
| pred = scaler_y.inverse_transform(pred_s)[0, 0] | |
| fig, ax = plt.subplots() | |
| ax.plot(range(10), vals.flatten(), marker='o', label='Input Revenue') | |
| ax.plot(10, pred, marker='X', markersize=10, color='red', label='Predicted Q10') | |
| ax.set_xlabel('Quarter Index (0-10)') | |
| ax.set_ylabel('Revenue') | |
| ax.set_title('Revenue Forecast') | |
| ax.legend() | |
| plt.tight_layout() | |
| return float(pred), fig | |
| # Build UI | |
| grid_css = """ | |
| body {background-color: #f7f7f7;} | |
| .gradio-container {max-width: 800px; margin: auto; padding: 20px;} | |
| h1, h2 {color: #333;} | |
| """ | |
| demo = gr.Blocks(css=grid_css) | |
| with demo: | |
| gr.Markdown("# 🚀 FinSight 360™ Dashboard") | |
| gr.Markdown("Comprehensive financial AI:\n- Bankruptcy Classification\n- Anomaly Scoring\n- Revenue Forecasting") | |
| with gr.Tab("🏦 Bankruptcy Classifier"): | |
| gr.Markdown("**Upload company features** (as DataFrame) to predict bankruptcy:") | |
| inp1 = gr.Dataframe(type="pandas", label="Features DataFrame") | |
| classify_btn = gr.Button("Run Classification") | |
| out1 = gr.Label(label="Predicted Label") | |
| plt1 = gr.Plot() | |
| classify_btn.click(fn=classify_fn, inputs=inp1, outputs=[out1, plt1]) | |
| with gr.Tab("📈 Anomaly Regression"): | |
| gr.Markdown("**Upload company features** (as DataFrame) to predict anomaly score:") | |
| inp2 = gr.Dataframe(type="pandas", label="Features DataFrame") | |
| regress_btn = gr.Button("Run Regression") | |
| out2 = gr.Textbox(label="Predicted Scores List") | |
| plt2 = gr.Plot() | |
| regress_btn.click(fn=regress_fn, inputs=inp2, outputs=[out2, plt2]) | |
| with gr.Tab("📊 LSTM Revenue Forecast"): | |
| gr.Markdown("**Enter last 10 quarterly revenues** (comma-separated) to forecast Q10 revenue:") | |
| inp3 = gr.Textbox(placeholder="e.g. 1000,1200,1100,...", label="Q0–Q9 Revenues") | |
| out3 = gr.Number(label="Predicted Q10 Revenue") | |
| plt3 = gr.Plot() | |
| inp3.submit(fn=lstm_fn, inputs=inp3, outputs=[out3, plt3]) | |
| gr.Markdown("---\n*Industry, Innovation and Infrastructure*") | |
| demo.launch() |