Update app.py

app.py

@@ -4,6 +4,8 @@ import logging
 import matplotlib.pyplot as plt
 import io
 import base64
+from PIL import Image
+import pandas as pd
 from simple_salesforce import Salesforce
 from dotenv import load_dotenv
 import requests
@@ -58,11 +60,29 @@ def generate_plot(planned_cost, actual_spend, forecast_cost):
     buf_gradio = io.BytesIO()
     plt.savefig(buf_gradio, format='png', bbox_inches='tight')
     buf_gradio.seek(0)
-    gradio_image = buf_gradio  # Gradio can handle the BytesIO object directly
+    # Convert BytesIO to PIL Image for Gradio
+    gradio_image = Image.open(buf_gradio)
     
     plt.close()
     return image_base64, gradio_image
 
+# Function to generate an Excel file with prediction results
+def generate_excel(planned_cost, actual_spend, forecast_cost, total_risk, insights, status, top_causes, forecast_cost_eur):
+    data = {
+        "Planned Cost ($)": [planned_cost],
+        "Actual Spend ($)": [actual_spend],
+        "Forecasted Cost ($)": [forecast_cost],
+        "Forecasted Cost (EUR)": [forecast_cost_eur if isinstance(forecast_cost_eur, str) else forecast_cost_eur],
+        "Total Risk": [total_risk],
+        "Insights": [insights],
+        "Status": [status],
+        "Top Causes": [top_causes]
+    }
+    df = pd.DataFrame(data)
+    excel_path = f"prediction_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
+    df.to_excel(excel_path, index=False)
+    return excel_path
+
 # Prediction function with input validation and formatted output
 def predict_risk(planned_cost, actual_spend, category, cement_index, labor_index, project_phase):
     # Validate numeric inputs
@@ -73,7 +93,7 @@ def predict_risk(planned_cost, actual_spend, category, cement_index, labor_index
         labor_index = float(labor_index) if labor_index else 0
     except ValueError:
         logger.error("Invalid input: Inputs must be numeric.")
-        return "Error: All numeric inputs (planned cost, actual spend, cement index, labor index) must be valid numbers.", None
+        return "Error: All numeric inputs (planned cost, actual spend, cement index, labor index) must be valid numbers.", None, None, None
 
     logger.debug(f"Starting prediction with inputs: planned_cost={planned_cost}, actual_spend={actual_spend}, "
                  f"category={category}, cement_index={cement_index}, labor_index={labor_index}, project_phase={project_phase}")
@@ -136,7 +156,7 @@ def predict_risk(planned_cost, actual_spend, category, cement_index, labor_index
 
     except Exception as e:
         logger.error(f"Failed to save to Salesforce: {str(e)}")
-        return f"Error: Failed to save to Salesforce: {str(e)}", None
+        return f"Error: Failed to save to Salesforce: {str(e)}", None, None, None
 
     # Fetch exchange rates
     try:
@@ -148,6 +168,9 @@ def predict_risk(planned_cost, actual_spend, category, cement_index, labor_index
         logger.error(f"Failed to fetch exchange rates: {str(e)}")
         forecast_cost_eur = "N/A"
 
+    # Generate Excel file
+    excel_file = generate_excel(planned_cost, actual_spend, forecast_cost, total_risk, insights, status, top_causes, forecast_cost_eur)
+
     # Format the output for Gradio
     risk_level = "High" if total_risk == 1 else "Low"
     output_text = (
@@ -164,24 +187,27 @@ def predict_risk(planned_cost, actual_spend, category, cement_index, labor_index
         f"Total Risk: {total_risk}\n"
         f"Forecasted Cost: ${forecast_cost:,.2f} (EUR: {forecast_cost_eur if isinstance(forecast_cost_eur, str) else f'{forecast_cost_eur:,.2f}'})\n"
         f"Top Causes: {top_causes}\n"
-        f"PDF Report: {pdf_url}"
+        f"PDF Report: {pdf_url}\n"
+        f"Excel Report: [Download link below]"
     )
-    return output_text, chart_image
+    return output_text, chart_image, pdf_url, excel_file
 
 # Gradio interface
 interface = gr.Interface(
     fn=predict_risk,
     inputs=[
         gr.Textbox(label="Planned Cost", placeholder="Enter planned cost (e.g., 1200000)"),
-        gr.Textbox(label="Actual Spend", placeholder="Enter actual spend (e.g., 25000000)"),
+        gr.Textbox(label="Actual Spend", placeholder="Enter actual spend (e.g., 5000000)"),
         gr.Dropdown(label="Category", choices=["Civil", "Electrical", "Plumbing", "Finishing", "Others"], value="Plumbing"),
-        gr.Textbox(label="Cement Index", placeholder="Enter cement index (e.g., 103)"),
-        gr.Textbox(label="Labor Index", placeholder="Enter labor index (e.g., 90)"),
+        gr.Textbox(label="Cement Index", placeholder="Enter cement index (e.g., 120)"),
+        gr.Textbox(label="Labor Index", placeholder="Enter labor index (e.g., 30)"),
         gr.Dropdown(label="Project Phase", choices=["Planning", "Execution", "Closure"], value="Planning")
     ],
     outputs=[
         gr.Textbox(label="Prediction Results"),
-        gr.Image(label="Forecast Chart")
+        gr.Image(label="Forecast Chart"),
+        gr.File(label="Download PDF Report"),
+        gr.File(label="Download Excel Report")
     ],
     title="Budget Overrun Risk Estimator",
     description="Enter project details to estimate budget overrun risk. All numeric fields are required."