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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
import pickle
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):
# df: pandas DataFrame of features
preds = xgb_clf.predict(df)
probs = xgb_clf.predict_proba(df)
# Bar chart for first prediction probabilities
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):
preds = xgb_reg.predict(df)
# Histogram of predictions
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):
# seq_str: comma-separated Q0-Q9 revenues
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]
# Plot input series + predicted point
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: Bankruptcy Classification, Anomaly Scoring, and Revenue Forecasting.")
with gr.Tab("🏦 Bankruptcy Classifier"):
gr.Markdown("Upload a single row of company features (CSV or paste) to predict bankruptcy:")
inp1 = gr.Dataframe(headers="...", datatype=["number"]*20 + ["text"]*4, label="Features DataFrame")
lbl1 = gr.Label(label="Predicted Label")
plt1 = gr.Plot()
inp1.submit(classify_fn, inp1, [lbl1, plt1])
with gr.Tab("📈 Anomaly Regression"):
gr.Markdown("Upload company features to predict anomaly score:")
inp2 = gr.Dataframe(headers="...", datatype=["number"]*100, label="Features DataFrame")
out2 = gr.Textbox(label="Predicted Scores List")
plt2 = gr.Plot()
inp2.submit(regress_fn, inp2, [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(lstm_fn, inp3, [out3, plt3])
gr.Markdown("---\n*Built with ❤️ by FinSight AI Team*")
demo.launch() |