Update yolo_training_script

yolo_training_script

@@ -1,107 +1 @@
-#!/usr/bin/env python3
-# This script is for YOLOv8 training using data from PlantNet and local files.
-
-import os
-import requests
-import json
-import random
-import shutil
-from ultralytics import YOLO
-
-# ==================== CONFIGURATION & SECRETS ====================
-# Use your API key from the main script
-PLANTNET_API_KEY = "2b10HlR0yP4wZbvHjP8jCo3Aae"
-PLANTNET_SEARCH_URL = "https://my-api.plantnet.org/v2/identify/all"
-
-# Define the training directory structure
-TRAIN_DIR = 'yolo_train'
-DATASET_YAML_PATH = os.path.join(TRAIN_DIR, 'dataset.yaml')
-
-# Define your 52 morphological features here.
-# This list is crucial and must match the labels you will create in your annotation tool.
-MORPHOLOGICAL_FEATURES = [
-    'leaf', 'stem', 'flower', 'fruit', 'root', 'branch', 'petal', 'sepal',
-    'bud', 'thorn', 'node', 'vein', 'blade', 'petiole', 'stamen', 'pistil',
-    'seed', 'bark', 'trunk', 'tuber', 'rhizome', 'bulb', 'spore', 'cone',
-    'tendril', 'pollen', 'nectar', 'bract', 'gemma', 'stomata', 'cuticle',
-    'xylem', 'phloem', 'vascular_bundle', 'cambium', 'lenticel', 'stolon',
-    'runner', 'corm', 'sheath', 'ligule', 'auricle', 'rachis', 'rachilla',
-    'glume', 'lemma', 'palea', 'floret', 'awns', 'caryopsis', 'pericarp', 'endosperm'
-]
-# You can update this list based on what you want to annotate.
-
-# ==================== DATA COLLECTION & PREPARATION ====================
-def download_and_prepare_dataset(plant_list: list, num_images_per_plant: int = 50):
-    """
-    Downloads and organizes images for a list of plants.
-    Note: PlantNet's API is for identification. We'll simulate fetching images
-    from a hypothetical image search to demonstrate the concept.
-    In a real-world scenario, you would use a web scraper or a curated dataset.
-    
-    This function creates a basic directory structure for YOLO.
-    """
-    print("Preparing dataset directory...")
-    # Clean up and create new directories
-    if os.path.exists(TRAIN_DIR):
-        shutil.rmtree(TRAIN_DIR)
-    os.makedirs(os.path.join(TRAIN_DIR, 'images'))
-    os.makedirs(os.path.join(TRAIN_DIR, 'images', 'train'))
-    os.makedirs(os.path.join(TRAIN_DIR, 'images', 'val'))
-    os.makedirs(os.path.join(TRAIN_DIR, 'labels'))
-    os.makedirs(os.path.join(TRAIN_DIR, 'labels', 'train'))
-    os.makedirs(os.path.join(TRAIN_DIR, 'labels', 'val'))
-
-    # Simulate downloading and creating placeholder images and labels
-    print("Simulating dataset creation. Please replace with your actual data and annotations.")
-    
-    # Create dataset.yaml
-    with open(DATASET_YAML_PATH, 'w') as f:
-        yaml_content = f"""
-path: {os.path.abspath(TRAIN_DIR)}
-train: images/train
-val: images/val
-
-names:
-  {os.linesep.join([f"  {i}: {name}" for i, name in enumerate(MORPHOLOGICAL_FEATURES)])}
-"""
-        f.write(yaml_content)
-    
-    print(f"✅ Created {DATASET_YAML_PATH}")
-    print(f"Next, populate {os.path.join(TRAIN_DIR, 'images', 'train')}, {os.path.join(TRAIN_DIR, 'images', 'val')}, and their corresponding 'labels' directories with your annotated data.")
-
-# ==================== YOLO TRAINING ====================
-def start_yolo_training():
-    """
-    Starts the YOLOv8 training process using the prepared dataset.
-    """
-    if not os.path.exists(DATASET_YAML_PATH):
-        print("❌ Dataset not found. Please run the data preparation step first.")
-        return
-
-    print("Loading YOLOv8 model for training...")
-    try:
-        model = YOLO('yolov8n.pt')
-        print("Starting training...")
-        
-        # Train the model
-        # You can adjust epochs, image size (imgsz), and other parameters
-        results = model.train(data=DATASET_YAML_PATH, epochs=100, imgsz=640)
-        
-        print("✅ Training complete. Results are saved in the 'runs' directory.")
-        print("The best model is located at 'runs/detect/train/weights/best.pt'.")
-        
-    except Exception as e:
-        print(f"An error occurred during training: {e}")
-
-# ==================== MAIN EXECUTION ====================
-if __name__ == "__main__":
-    # Example list of plants to train on
-    plants_to_train = ["rosemary", "basil", "mint"]
-    
-    # Step 1: Prepare the directory structure and dataset.yaml
-    # This function is a placeholder. You need to manually add your images and labels.
-    download_and_prepare_dataset(plants_to_train)
-    
-    # Step 2: Start the training process
-    # Make sure you have your annotated images and labels in place before running this.
-    # start_yolo_training() # Uncomment this line to run the training
+pip install --upgrade gradio