Update app.py

app.py

@@ -7,91 +7,322 @@ from tensorflow.keras.models import load_model
 from sklearn.preprocessing import MinMaxScaler
 import matplotlib.pyplot as plt
 import seaborn as sns
+import io
 
 # 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;}
+# Set matplotlib style for dark theme compatibility
+plt.style.use('dark_background')
+
+def process_csv_file(file):
+    """Process uploaded CSV file and return DataFrame"""
+    if file is None:
+        return None
+    try:
+        df = pd.read_csv(file.name)
+        return df
+    except Exception as e:
+        gr.Warning(f"Error reading CSV file: {str(e)}")
+        return None
+
+def classify_fn(file):
+    """Bankruptcy classification from CSV file"""
+    if file is None:
+        return "Please upload a CSV file", None
+    
+    df = process_csv_file(file)
+    if df is None:
+        return "Error processing file", None
+    
+    try:
+        # Use all rows in the CSV for prediction
+        preds = xgb_clf.predict(df)
+        probs = xgb_clf.predict_proba(df)
+        
+        # Create visualization
+        fig, ax = plt.subplots(figsize=(10, 6), facecolor='#1f1f1f')
+        ax.set_facecolor('#1f1f1f')
+        
+        if len(preds) == 1:
+            # Single company prediction
+            bars = ax.bar(['No Bankruptcy', 'Bankruptcy'], probs[0], 
+                         color=['#4CAF50', '#F44336'], alpha=0.8)
+            ax.set_ylim(0, 1)
+            ax.set_title('Bankruptcy Probability', color='white', fontsize=14)
+            ax.set_ylabel('Probability', color='white')
+            result_text = f"Prediction: {'Bankruptcy Risk' if preds[0] == 1 else 'No Bankruptcy Risk'}\nConfidence: {max(probs[0]):.2%}"
+        else:
+            # Multiple companies
+            bankruptcy_count = np.sum(preds)
+            safe_count = len(preds) - bankruptcy_count
+            bars = ax.bar(['Safe Companies', 'At Risk Companies'], 
+                         [safe_count, bankruptcy_count], 
+                         color=['#4CAF50', '#F44336'], alpha=0.8)
+            ax.set_title(f'Bankruptcy Analysis for {len(preds)} Companies', color='white', fontsize=14)
+            ax.set_ylabel('Number of Companies', color='white')
+            result_text = f"Total Companies: {len(preds)}\nSafe: {safe_count}\nAt Risk: {bankruptcy_count}"
+        
+        ax.tick_params(colors='white')
+        ax.spines['bottom'].set_color('white')
+        ax.spines['left'].set_color('white')
+        ax.spines['top'].set_visible(False)
+        ax.spines['right'].set_visible(False)
+        
+        plt.tight_layout()
+        return result_text, fig
+        
+    except Exception as e:
+        return f"Error in prediction: {str(e)}", None
+
+def regress_fn(file):
+    """Anomaly detection from CSV file"""
+    if file is None:
+        return "Please upload a CSV file", None
+    
+    df = process_csv_file(file)
+    if df is None:
+        return "Error processing file", None
+    
+    try:
+        preds = xgb_reg.predict(df)
+        
+        # Create visualization
+        fig, ax = plt.subplots(figsize=(10, 6), facecolor='#1f1f1f')
+        ax.set_facecolor('#1f1f1f')
+        
+        sns.histplot(preds, bins=20, kde=True, ax=ax, color='#00BCD4', alpha=0.7)
+        ax.set_title('Anomaly Score Distribution', color='white', fontsize=14)
+        ax.set_xlabel('Anomaly Score', color='white')
+        ax.set_ylabel('Frequency', color='white')
+        ax.tick_params(colors='white')
+        ax.spines['bottom'].set_color('white')
+        ax.spines['left'].set_color('white')
+        ax.spines['top'].set_visible(False)
+        ax.spines['right'].set_visible(False)
+        
+        plt.tight_layout()
+        
+        # Summary statistics
+        avg_score = np.mean(preds)
+        high_risk_count = np.sum(preds > np.percentile(preds, 75))
+        result_text = f"Average Anomaly Score: {avg_score:.3f}\nHigh Risk Companies: {high_risk_count}/{len(preds)}\nScore Range: {np.min(preds):.3f} - {np.max(preds):.3f}"
+        
+        return result_text, fig
+        
+    except Exception as e:
+        return f"Error in prediction: {str(e)}", None
+
+def lstm_fn(file):
+    """LSTM revenue forecasting from CSV file"""
+    if file is None:
+        return "Please upload a CSV file", None
+    
+    df = process_csv_file(file)
+    if df is None:
+        return "Error processing file", None
+    
+    try:
+        # Expect CSV with revenue columns or a single row with 10 revenue values
+        if df.shape[1] < 10:
+            return "CSV must contain at least 10 revenue columns for quarterly data", None
+        
+        # Take first row and first 10 columns as revenue sequence
+        vals = df.iloc[0, :10].values.astype(float).reshape(1, -1)
+        
+        # Scale and predict
+        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]
+        
+        # Create visualization
+        fig, ax = plt.subplots(figsize=(12, 6), facecolor='#1f1f1f')
+        ax.set_facecolor('#1f1f1f')
+        
+        quarters = [f'Q{i+1}' for i in range(10)]
+        ax.plot(quarters, vals.flatten(), marker='o', linewidth=2, 
+                markersize=8, color='#2196F3', label='Historical Revenue')
+        ax.plot('Q11', pred, marker='X', markersize=15, color='#FF5722', 
+                label=f'Predicted Q11: ${pred:,.0f}')
+        
+        ax.set_xlabel('Quarter', color='white')
+        ax.set_ylabel('Revenue ($)', color='white')
+        ax.set_title('Revenue Forecast - Next Quarter Prediction', color='white', fontsize=14)
+        ax.legend(facecolor='#2f2f2f', edgecolor='white', labelcolor='white')
+        ax.tick_params(colors='white')
+        ax.spines['bottom'].set_color('white')
+        ax.spines['left'].set_color('white')
+        ax.spines['top'].set_visible(False)
+        ax.spines['right'].set_visible(False)
+        ax.grid(True, alpha=0.3, color='white')
+        
+        plt.xticks(rotation=45)
+        plt.tight_layout()
+        
+        # Calculate growth rate
+        last_revenue = vals.flatten()[-1]
+        growth_rate = ((pred - last_revenue) / last_revenue) * 100
+        result_text = f"Predicted Q11 Revenue: ${pred:,.0f}\nGrowth from Q10: {growth_rate:+.1f}%"
+        
+        return result_text, fig
+        
+    except Exception as e:
+        return f"Error in prediction: {str(e)}", None
+
+# Custom CSS for proper dark mode support
+custom_css = """
+/* Dark theme for the entire interface */
+.gradio-container {
+    background-color: #1a1a1a !important;
+    color: #ffffff !important;
+}
+
+.gr-box {
+    background-color: #2d2d2d !important;
+    border: 1px solid #404040 !important;
+}
+
+.gr-form {
+    background-color: #2d2d2d !important;
+}
+
+.gr-panel {
+    background-color: #2d2d2d !important;
+    border: 1px solid #404040 !important;
+}
+
+.gr-button {
+    background-color: #0066cc !important;
+    color: white !important;
+    border: none !important;
+}
+
+.gr-button:hover {
+    background-color: #0052a3 !important;
+}
+
+.gr-input, .gr-textbox {
+    background-color: #2d2d2d !important;
+    border: 1px solid #404040 !important;
+    color: #ffffff !important;
+}
+
+.gr-upload {
+    background-color: #2d2d2d !important;
+    border: 2px dashed #404040 !important;
+    color: #ffffff !important;
+}
+
+.gr-file {
+    background-color: #2d2d2d !important;
+    color: #ffffff !important;
+}
+
+/* Tab styling */
+.gr-tab-nav {
+    background-color: #2d2d2d !important;
+    border-bottom: 1px solid #404040 !important;
+}
+
+.gr-tab-nav button {
+    background-color: transparent !important;
+    color: #ffffff !important;
+    border: none !important;
+}
+
+.gr-tab-nav button.selected {
+    background-color: #0066cc !important;
+    color: white !important;
+}
+
+/* Text and markdown */
+.gr-markdown {
+    color: #ffffff !important;
+}
+
+.gr-markdown h1, .gr-markdown h2, .gr-markdown h3 {
+    color: #ffffff !important;
+}
+
+/* Ensure plot backgrounds work with dark theme */
+.gr-plot {
+    background-color: #1f1f1f !important;
+}
 """
 
-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()
+# Create the Gradio interface
+with gr.Blocks(css=custom_css, theme=gr.themes.Base(), title="TriCast AI") as demo:
+    gr.Markdown("""
+    # 🚀 TriCast AI
+    ### Advanced Financial Intelligence Platform
+    Upload your company's financial data as a CSV file to get comprehensive AI-powered insights across three key areas.
+    """)
+    
+    gr.Markdown("""
+    **📁 CSV File Format Guidelines:**
+    - **Bankruptcy & Anomaly Detection**: Include financial metrics as columns (revenue, debt, assets, etc.)
+    - **Revenue Forecasting**: First 10 columns should contain quarterly revenue data
+    - Each row represents one company's data
+    """)
+    
+    with gr.Tab("🏦 Bankruptcy Risk Assessment"):
+        gr.Markdown("**Upload CSV with company financial data to assess bankruptcy risk**")
+        with gr.Row():
+            with gr.Column():
+                file1 = gr.File(label="Upload CSV File", file_types=[".csv"])
+                classify_btn = gr.Button("🔍 Analyze Bankruptcy Risk", variant="primary")
+            with gr.Column():
+                out1 = gr.Textbox(label="Analysis Results", lines=4)
+                plt1 = gr.Plot(label="Risk Visualization")
+        classify_btn.click(fn=classify_fn, inputs=file1, outputs=[out1, plt1])
+    
+    with gr.Tab("📊 Anomaly Detection"):
+        gr.Markdown("**Upload CSV with company financial data to detect anomalies**")
+        with gr.Row():
+            with gr.Column():
+                file2 = gr.File(label="Upload CSV File", file_types=[".csv"])
+                regress_btn = gr.Button("🔎 Detect Anomalies", variant="primary")
+            with gr.Column():
+                out2 = gr.Textbox(label="Anomaly Analysis", lines=4)
+                plt2 = gr.Plot(label="Score Distribution")
+        regress_btn.click(fn=regress_fn, inputs=file2, outputs=[out2, plt2])
+    
+    with gr.Tab("📈 Revenue Forecasting"):
+        gr.Markdown("**Upload CSV with quarterly revenue data (10 quarters) to forecast next quarter**")
+        with gr.Row():
+            with gr.Column():
+                file3 = gr.File(label="Upload CSV File", file_types=[".csv"])
+                forecast_btn = gr.Button("📊 Forecast Revenue", variant="primary")
+            with gr.Column():
+                out3 = gr.Textbox(label="Forecast Results", lines=4)
+                plt3 = gr.Plot(label="Revenue Trend & Prediction")
+        forecast_btn.click(fn=lstm_fn, inputs=file3, outputs=[out3, plt3])
+    
+    with gr.Tab("📋 Sample Data Format"):
+        gr.Markdown("""
+        ### Sample CSV Formats:
+        
+        **For Bankruptcy & Anomaly Detection:**
+        ```
+        company_name,total_assets,total_liabilities,revenue,debt_ratio,current_ratio
+        Company A,1000000,500000,800000,0.5,2.1
+        Company B,2000000,1800000,600000,0.9,0.8
+        ```
+        
+        **For Revenue Forecasting:**
+        ```
+        q1_revenue,q2_revenue,q3_revenue,q4_revenue,q5_revenue,q6_revenue,q7_revenue,q8_revenue,q9_revenue,q10_revenue
+        100000,120000,110000,130000,125000,140000,135000,150000,145000,160000
+        ```
+        """)
+    
+    gr.Markdown("---")
+    gr.Markdown("*TriCast AI - Powered by Advanced Machine Learning | Industry, Innovation and Infrastructure*")
+
+if __name__ == "__main__":
+    demo.launch()