Update app.py

app.py

@@ -12,6 +12,9 @@ from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
 from reportlab.lib.utils import simpleSplit
 from io import BytesIO
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
 
 # Hardcoded mappings
 weather_map = {"Cloudy": 0, "Rainy": 1, "Sunny": 2}
@@ -62,7 +65,46 @@ except Exception as e:
     print(f"Error training model: {e}")
     raise
 
-# Function to generate simple PDF and return base64-encoded string with text wrapping
+# Function to generate heatmap and return path
+def generate_heatmap(phase, weather, model):
+    print("Generating heatmap...")
+    try:
+        # Prepare data for heatmap
+        absentee_range = np.linspace(0, 100, 20)
+        delay_log_range = np.linspace(0, 20, 20)
+        framing = 1 if phase == "Framing" else 0
+        foundation = 1 if phase == "Foundation" else 0
+        weather_encoded = weather_map.get(weather, 0)
+        
+        # Generate predictions for heatmap
+        Z = np.zeros((len(delay_log_range), len(absentee_range)))
+        for i, delay_log in enumerate(delay_log_range):
+            for j, absentee in enumerate(absentee_range):
+                input_data = [[framing, foundation, weather_encoded, absentee, delay_log]]
+                Z[i, j] = model.predict(input_data)[0]
+        
+        # Create heatmap
+        plt.figure(figsize=(8, 6))
+        sns.heatmap(Z, xticklabels=np.round(absentee_range, 1), yticklabels=np.round(delay_log_range, 1),
+                    cmap="YlOrRd", annot=True, fmt=".1f", cbar_kws={'label': 'Predicted Delay %'})
+        plt.xlabel("Absentee %")
+        plt.ylabel("Previous Delay Log")
+        plt.title(f"Delay Prediction Heatmap (Phase: {phase}, Weather: {weather})")
+        
+        # Save heatmap
+        output_dir = "pdf_reports"
+        os.makedirs(output_dir, exist_ok=True)
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        heatmap_path = os.path.join(output_dir, f"heatmap_{timestamp}.png")
+        plt.savefig(heatmap_path, bbox_inches='tight')
+        plt.close()
+        print(f"Heatmap saved at: {heatmap_path}")
+        return heatmap_path
+    except Exception as e:
+        print(f"Heatmap generation failed: {e}")
+        return None
+
+# Function to generate simple PDF with heatmap and return base64-encoded string
 def generate_pdf_report(phase, weather, absentee_pct, delay_log, prediction, risk, insight):
     print("Generating PDF report...")
     try:
@@ -83,7 +125,7 @@ def generate_pdf_report(phase, weather, absentee_pct, delay_log, prediction, ris
             f"Previous Delay Log: {delay_log}",
             f"Predicted Delay: {prediction}%",
             f"Risk Level: {risk}",
-            f"AI Insight: {insight}"
+            "AI Insight:"
         ]
         
         # Wrap and draw each line properly
@@ -91,7 +133,20 @@ def generate_pdf_report(phase, weather, absentee_pct, delay_log, prediction, ris
             lines = simpleSplit(line, 'Helvetica', 12, max_width)
             for wrapped_line in lines:
                 c.drawString(100, y_position, wrapped_line)
-                y_position -= 16  # move down after each wrapped line
+                y_position -= 16
+        
+        # Wrap and draw insight (which may be long)
+        insight_lines = simpleSplit(insight, 'Helvetica', 12, max_width)
+        for wrapped_line in insight_lines:
+            c.drawString(100, y_position, wrapped_line)
+            y_position -= 16
+        
+        # Add heatmap
+        heatmap_path = generate_heatmap(phase, weather, model)
+        if heatmap_path and os.path.exists(heatmap_path):
+            c.drawString(100, y_position - 20, "Delay Prediction Heatmap:")
+            c.drawImage(heatmap_path, 100, y_position - 250, width=400, height=200)
+            y_position -= 270
         
         c.showPage()
         c.save()
@@ -108,10 +163,10 @@ def generate_pdf_report(phase, weather, absentee_pct, delay_log, prediction, ris
             f.write(pdf_data)
         print(f"PDF saved locally at: {output_path}")
         
-        return pdf_base64, output_path
+        return pdf_base64, output_path, heatmap_path
     except Exception as e:
         print(f"PDF generation failed: {e}")
-        return None, None
+        return None, None, None
 
 # Main prediction function
 def predict_delay(phase, weather, absentee_pct, delay_log):
@@ -132,21 +187,67 @@ def predict_delay(phase, weather, absentee_pct, delay_log):
         prediction = model.predict(input_data)[0]
         prediction = round(prediction, 2)
 
+        # Tailored AI Insights
         if prediction >= 75:
             risk = "High Risk"
+            insight = f"High delay risk ({prediction}%) in {phase} phase under {weather} conditions. "
+            if absentee_pct > 30:
+                insight += f"High absenteeism ({absentee_pct}%) is a major factor. Hire temporary workers or offer overtime incentives. "
+            else:
+                insight += f"Absenteeism ({absentee_pct}%) is moderate; ensure key staff are present for critical {phase} tasks. "
+            if delay_log > 5:
+                insight += f"Significant past delays ({delay_log}) detected; conduct a root cause analysis to address bottlenecks. "
+            else:
+                insight += f"Past delays ({delay_log}) are manageable; review task dependencies to prevent escalation. "
+            if weather == "Rainy":
+                insight += "Rainy weather increases risk; use protective coverings or shift to indoor tasks."
+            elif weather == "Cloudy":
+                insight += "Cloudy weather may slow progress; monitor conditions and prepare for potential rain."
+            else:
+                insight += "Sunny weather is optimal; maximize outdoor work to reduce delays."
         elif prediction >= 50:
             risk = "Moderate Risk"
+            insight = f"Moderate delay risk ({prediction}%) in {phase} phase under {weather} conditions. "
+            if absentee_pct > 30:
+                insight += f"High absenteeism ({absentee_pct}%) needs attention; consider cross-training staff to cover gaps. "
+            elif absentee_pct < 10:
+                insight += f"Low absenteeism ({absentee_pct}%) is good; maintain attendance with morale-boosting measures. "
+            else:
+                insight += f"Moderate absenteeism ({absentee_pct}%) suggests reviewing workforce allocation for {phase} tasks. "
+            if delay_log > 5:
+                insight += f"Past delays ({delay_log}) indicate inefficiencies; streamline workflows in {phase}. "
+            else:
+                insight += f"Past delays ({delay_log}) are low; ensure timely material delivery to maintain progress. "
+            if weather == "Rainy":
+                insight += "Rainy weather may disrupt work; schedule flexible tasks and secure equipment."
+            elif weather == "Cloudy":
+                insight += "Cloudy weather is manageable; keep weather monitoring active."
+            else:
+                insight += "Sunny weather supports progress; optimize daily schedules to leverage good conditions."
         else:
             risk = "Low Risk"
+            insight = f"Low delay risk ({prediction}%) in {phase} phase under {weather} conditions. "
+            if absentee_pct > 30:
+                insight += f"Despite low risk, high absenteeism ({absentee_pct}%) could escalate; monitor attendance closely. "
+            else:
+                insight += f"Absenteeism ({absentee_pct}%) is under control; sustain with regular team check-ins. "
+            if delay_log > 5:
+                insight += f"Past delays ({delay_log}) are notable; maintain vigilance to prevent recurrence in {phase}. "
+            else:
+                insight += f"Minimal past delays ({delay_log}); continue efficient task management in {phase}. "
+            if weather == "Rainy":
+                insight += "Rainy weather could pose minor risks; have contingency plans ready."
+            elif weather == "Cloudy":
+                insight += "Cloudy weather is unlikely to cause issues; maintain standard operations."
+            else:
+                insight += "Sunny weather is ideal; capitalize on it to stay ahead of schedule."
 
-        insight = f"Phase: {phase}, Weather: {weather}, Absenteeism: {absentee_pct}%, Previous Delay: {delay_log} → Risk: {risk}"
-
-        pdf_base64, pdf_path = generate_pdf_report(phase, weather, absentee_pct, delay_log, prediction, risk, insight)
+        pdf_base64, pdf_path, heatmap_path = generate_pdf_report(phase, weather, absentee_pct, delay_log, prediction, risk, insight)
 
-        return prediction, risk, insight, pdf_base64, pdf_path
+        return prediction, risk, insight, pdf_base64, pdf_path, heatmap_path
     except Exception as e:
         print(f"Prediction error: {e}")
-        return None, None, f"Error: {e}", None, None
+        return None, None, f"Error: {e}", None, None, None
 
 # FastAPI for Salesforce
 api_app = FastAPI()
@@ -161,7 +262,7 @@ async def predict_from_salesforce(request: Request):
         absentee_pct = data.get("absentee_pct", 0)
         delay_log = data.get("delay_log", 0)
 
-        prediction, risk, insight, pdf_base64, pdf_path = predict_delay(phase, weather, absentee_pct, delay_log)
+        prediction, risk, insight, pdf_base64, pdf_path, heatmap_path = predict_delay(phase, weather, absentee_pct, delay_log)
 
         if prediction is None:
             return JSONResponse(status_code=500, content={"status": "error", "message": insight})
@@ -172,6 +273,7 @@ async def predict_from_salesforce(request: Request):
             "ai_insight": insight,
             "pdf_report_base64": pdf_base64 if pdf_base64 else "",
             "pdf_local_path": pdf_path if pdf_path else "PDF generation failed",
+            "heatmap_path": heatmap_path if heatmap_path else "Heatmap generation failed",
             "status": "success"
         })
     except Exception as e:
@@ -194,11 +296,16 @@ try:
 
         def predict_and_format(phase, weather, absentee, delay_log):
             print("Gradio predict button clicked.")
-            prediction, risk, insight, pdf_base64, pdf_path = predict_delay(phase, weather, absentee, delay_log)
+            prediction, risk, insight, pdf_base64, pdf_path, heatmap_path = predict_delay(phase, weather, absentee, delay_log)
             if prediction is None:
                 return f"Error: {insight}"
-            return f"Predicted Delay: {prediction}%\nRisk Level: {risk}\nInsight: {insight}\nPDF Report: {'Saved locally at ' + pdf_path if pdf_path else 'Failed to generate'}\nPDF Base64: {'Generated' if pdf_base64 else 'Not generated'}"
-
+            return (f"Predicted Delay: {prediction}%\n"
+                    f"Risk Level: {risk}\n"
+                    f"Insight: {insight}\n"
+                    f"PDF Report: {'Saved locally at ' + pdf_path if pdf_path else 'Failed to generate'}\n"
+                    f"Heatmap: {'Saved locally at ' + heatmap_path if heatmap_path else 'Failed to generate'}\n"
+                    f"PDF Base64: {'Generated' if pdf_base64 else 'Not generated'}")
+        
         submit.click(
             predict_and_format,
             inputs=[phase_input, weather_input, absentee_input, delay_input],