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718f018 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | import os
import numpy as np
import tensorflow as tf
from PIL import Image
from django.conf import settings
# Define the path to the model
MODEL_PATH = os.path.join(settings.BASE_DIR, 'model', 'efficientnet_b0.h5')
model = None
# Load the model once when the server starts
try:
model = tf.keras.models.load_model(MODEL_PATH)
print("✅ ML Model Loaded Successfully")
except Exception as e:
print(f"⚠️ ML Model not found or error loading: {e}. Using dummy mode.")
def predict_xray(image_file):
"""
Predicts if an X-ray image is Positive (Tuberculosis) or Negative (Normal).
"""
if not model:
# Dummy fallback if model didn't load
return "Negative", 85.5, "Low"
try:
# 1. Load and Preprocess Image
# Convert to RGB to ensure 3 channels
img = Image.open(image_file).convert('RGB')
# Resize to 128x128 (Must match your training notebook size)
img = img.resize((128, 128))
# Convert to array and add batch dimension
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
# CRITICAL FIX: Normalize pixel values to 0-1 range
img_array = img_array / 255.0
# 2. Make Prediction
# Expected output shape: [[prob_normal, prob_tb]]
prediction = model.predict(img_array)
# Get the class with the highest probability
# Class 0 = Normal, Class 1 = Tuberculosis
class_idx = np.argmax(prediction, axis=1)[0]
confidence = float(np.max(prediction))
# 3. Interpret Results
if class_idx == 1:
result = "Positive" # Tuberculosis detected
if confidence > 0.8:
risk_level = "High"
elif confidence >= 0.5:
risk_level = "Medium"
else:
risk_level = "Low"
else:
result = "Negative" # Normal
risk_level = "Low"
return (result, round(confidence * 100, 2), risk_level)
except Exception as e:
print(f"❌ Error processing image: {e}")
# Return error state or safe default
return "Error", 0.0, "Low" |