Added learning model for image generation

image_generation.py

@@ -0,0 +1,58 @@
+import os
+import pandas as pd
+import random
+
+from streamlit import echo
+
+METADATA_CSV = ".\plant_growth_data\plant_growth_data.csv"
+df = pd.read_csv(METADATA_CSV)
+DATASET_DIR = ".\plant_growth_data\dataset"
+PLANTVILLAGE_DIR = ".\plant_growth_data\images"
+
+
+def determine_health_status(description: str) -> str:
+    illness_keywords = [
+        "disease", "wilt", "brown", "yellowing", "withered", "damage", "ill", "unhealthy"
+    ]
+    healthy_keywords = [
+        "healthy", "vibrant", "green", "lush", "strong", "thriving", "fresh", "good condition"
+    ]
+    desc_lower = description.lower()
+    if any(word in desc_lower for word in healthy_keywords):
+        return "healthy"
+    if any(word in desc_lower for word in illness_keywords):
+        return "ill"
+    return "healthy"
+
+def filter_images_for_row(plant_folder):
+    images = os.listdir(plant_folder)
+    return images
+
+def generate_image(plant, description, plant_age):
+    plant_health_status = determine_health_status(description)
+
+    plant_map = {
+        "Basil": "basil\\" + plant_health_status,
+        "Tomato": "tomato\\" + plant_health_status,
+        "Lettuce": "lettuce\\" + plant_health_status,
+    }
+
+    folder_name = plant_map.get(plant)
+    if folder_name is None:
+        raise ValueError(f"Pianta '{plant}' non trovata nel dataset.")
+
+    plant_folder = os.path.join(PLANTVILLAGE_DIR, folder_name)
+    if not os.path.exists(plant_folder):
+        raise FileNotFoundError(f"Cartella {plant_folder} non trovata.")
+
+    candidate_images = filter_images_for_row(plant_folder)
+    if not candidate_images:
+        raise FileNotFoundError(f"Nessuna immagine trovata nella cartella {plant_folder}.")
+
+    if plant_age < 30:
+        chosen_image = [img for img in candidate_images if "young" in img]
+    else:
+        chosen_image = [img for img in candidate_images if "mature" in img]
+  
+    image_path = os.path.join(plant_folder, chosen_image[0])
+    return image_path