115 lines
No EOL
4 KiB
Python
115 lines
No EOL
4 KiB
Python
import io
|
|
import traceback
|
|
from flask import Flask, request, jsonify
|
|
from PIL import Image
|
|
import numpy as np
|
|
import torch
|
|
import clip
|
|
import tensorflow as tf
|
|
|
|
# --- Configuration ---
|
|
class Config:
|
|
"""Groups all required configuration variables in one place."""
|
|
PLANT_MODEL_PATH = 'models/BestModel.keras'
|
|
|
|
PLANT_CLASSES = ['tomato', 'basil', 'mint', 'lettuce', 'rosemary', 'strawberry']
|
|
IMG_SIZE = (384, 384)
|
|
|
|
# Health analysis prompt
|
|
HEALTH_PROMPTS = [
|
|
"a photo of a healthy {plant} plant with vibrant green leaves",
|
|
"a photo of a sick {plant} plant with yellow spots or discoloration",
|
|
"a photo of a dehydrated {plant} plant with wilted or drooping leaves",
|
|
"a photo of a dead {plant} plant with brown, dry, or crispy leaves"
|
|
]
|
|
HEALTH_LABELS = ["Healthy", "Diseased", "Dehydrated", "Dead"]
|
|
|
|
# --- Application Setup ---
|
|
app = Flask(__name__)
|
|
|
|
# --- Model Loading ---
|
|
def load_models():
|
|
"""Loads and initializes all required machine learning models."""
|
|
# Load plant identification model
|
|
print("1. Loading plant identification model...")
|
|
plant_model = tf.keras.models.load_model(Config.PLANT_MODEL_PATH)
|
|
|
|
# Load model for health analysis
|
|
print("2. Loading CLIP model for health analysis...")
|
|
device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
clip_model, clip_preprocess = clip.load("ViT-B/32", device=device)
|
|
|
|
print("\nAll models loaded successfully.")
|
|
return plant_model, clip_model, clip_preprocess, device
|
|
|
|
# Load models
|
|
plant_model, clip_model, clip_preprocess, device = load_models()
|
|
|
|
# --- Core ML Functions ---
|
|
def identify_plant(image):
|
|
"""Plant identification"""
|
|
img = image.resize(Config.IMG_SIZE)
|
|
img_array = tf.keras.preprocessing.image.img_to_array(img)
|
|
img_array = tf.expand_dims(img_array, 0)
|
|
img_array = tf.keras.applications.mobilenet_v2.preprocess_input(img_array)
|
|
|
|
preds = plant_model.predict(img_array, verbose=0)
|
|
best_idx = np.argmax(preds[0])
|
|
|
|
plant_name = Config.PLANT_CLASSES[best_idx]
|
|
confidence = float(np.max(preds))
|
|
|
|
return plant_name, confidence
|
|
|
|
def assess_health(plant_name, image):
|
|
"""Plant health"""
|
|
prompts = [p.format(plant=plant_name) for p in Config.HEALTH_PROMPTS]
|
|
image_input = clip_preprocess(image).unsqueeze(0).to(device)
|
|
text_tokens = clip.tokenize(prompts).to(device)
|
|
|
|
with torch.no_grad():
|
|
image_features = clip_model.encode_image(image_input)
|
|
text_features = clip_model.encode_text(text_tokens)
|
|
|
|
image_features /= image_features.norm(dim=-1, keepdim=True)
|
|
text_features /= text_features.norm(dim=-1, keepdim=True)
|
|
|
|
similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
|
|
probs = similarity.cpu().numpy()[0]
|
|
|
|
status = Config.HEALTH_LABELS[np.argmax(probs)]
|
|
confidence = float(np.max(probs))
|
|
probabilities = {label: float(p) for label, p in zip(Config.HEALTH_LABELS, probs)}
|
|
|
|
return status, confidence, probabilities
|
|
|
|
# --- API Endpoint ---
|
|
@app.route('/analyze', methods=['POST'])
|
|
def analyze_plant_image():
|
|
"""Image analysis."""
|
|
if 'image' not in request.files:
|
|
return jsonify({'error': 'No image file provided'}), 400
|
|
|
|
try:
|
|
file = request.files['image']
|
|
image = Image.open(io.BytesIO(file.read())).convert("RGB")
|
|
|
|
plant_name, plant_conf = identify_plant(image.copy())
|
|
health_status, health_conf, health_probs = assess_health(plant_name, image)
|
|
|
|
return jsonify({
|
|
'plant_species': plant_name,
|
|
'identification_confidence': f"{plant_conf:.2%}",
|
|
'health_status': health_status,
|
|
'health_confidence': f"{health_conf:.2%}",
|
|
'health_breakdown': health_probs
|
|
})
|
|
|
|
except Exception as e:
|
|
print("An error occurred:", str(e))
|
|
traceback.print_exc()
|
|
return jsonify({'error': 'An internal server error occurred.'}), 500
|
|
|
|
# --- Main Execution ---
|
|
if __name__ == '__main__':
|
|
app.run(host='0.0.0.0', port=5000, debug=True) |