added isolated function to merge into the dashboard

forDashboardIntegration.py

@@ -0,0 +1,42 @@
+from datetime import datetime
+from script import PlantPredictor
+
+def dashboard_plant_prediction(image_path, start_date, end_date, additional_notes=""):
+    """
+    Simple function for dashboard integration
+    """
+    try:
+        # Calculate days
+        start_dt = datetime.strptime(start_date, "%Y-%m-%d")
+        end_dt = datetime.strptime(end_date, "%Y-%m-%d")
+        days = (end_dt - start_dt).days
+        
+        if days <= 0:
+            return {"success": False, "error": "Invalid date range"}
+        
+        # Create predictor and run
+        predictor = PlantPredictor()
+        result = predictor.dashboard_plant_prediction(image_path, days, additional_notes)
+        
+        if result:
+            return {"success": True, "result": result}
+        else:
+            return {"success": False, "error": "No result"}
+            
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+
+# Test
+if __name__ == "__main__":
+    result = dashboard_plant_prediction(
+        "./basilico.jpg", 
+        "2024-08-01", 
+        "2024-08-08",
+        "Test plant"
+    )
+    
+    if result["success"]:
+        print(" SUCCESS!")
+    else:
+        print(f" ERROR: {result['error']}")

script.py

@@ -0,0 +1,60 @@
+from datetime import datetime
+import sys
+import os
+
+class PlantPredictor:
+    def dashboard_plant_prediction(
+        image_path: str,
+        start_date: str,
+        end_date: str,
+        additional_notes: str = ""
+    ) -> dict:
+        try:
+            # Calcola giorni
+            start_dt = datetime.strptime(start_date, "%Y-%m-%d")
+            end_dt = datetime.strptime(end_date, "%Y-%m-%d")
+            days = (end_dt - start_dt).days
+            if days <= 0:
+                return {"success": False, "error": "End date must be after start date", "days": days}
+
+            # Log
+            print(f"Dashboard prediction request: {start_date} to {end_date} ({days} days) image={image_path}")
+            if additional_notes:
+                print(f"Notes: {additional_notes}")
+
+            # Inizializza il predictor e chiama il metodo
+            predictor = PlantPredictor()
+            result = predictor.predict_plant_growth(image_path, days, additional_notes)
+
+            # Unwrap risultato tuple
+            if isinstance(result, tuple) and len(result) == 5:
+                _img, conditions, weather_df, plant_type, plant_health = result
+                return {
+                    "success": True,
+                    "plant_analysis": {"plant_type": plant_type, "plant_health": plant_health},
+                    "weather_conditions": conditions,
+                    "weather_data_shape": weather_df.shape,
+                    "parameters_used": {"start_date": start_date, "end_date": end_date, "days": days, "notes": additional_notes, "image": image_path},
+                    "prediction_summary": {
+                        "temperature_range": f"{conditions['avg_temp_min']}–{conditions['avg_temp_max']}°C",
+                        "total_rain": f"{conditions['total_rain']}mm",
+                        "sunshine_hours": f"{conditions['total_sunshine_hours']}h"
+                    }
+                }
+            else:
+                return {"success": False, "error": "Invalid result from PlantPredictor", "result": result}
+
+        except ValueError as e:
+            return {"success": False, "error": f"Date format error: {e}"}
+        except Exception as e:
+            return {"success": False, "error": f"Unexpected error: {e}"}
+
+# Esempio di test
+if __name__ == '__main__':
+    res = dashboard_plant_prediction(
+        image_path='./basilico.jpg',
+        start_date='2024-08-01',
+        end_date='2024-08-08',
+        additional_notes='Indoor day 3'
+    )
+    print(res)