File size: 6,187 Bytes
8209f26 | 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 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | from PIL import Image
import numpy as np
from fastapi import File, UploadFile
import os
import tensorflow as tf
from fastapi.middleware.cors import CORSMiddleware
from fastapi import FastAPI
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Animal names here
ANIMALS = ['Cat', 'Dog', 'Panda']
# Model path - check multiple possible locations for different model formats
model_path = None
model_type = None # 'savedmodel' or 'keras'
# Check for SavedModel format (try both old and new naming conventions)
savedmodel_paths = [
"animal-classification/INPUT_model_path/animal-cnn/savedmodel",
"animal-classification/animal-cnn/savedmodel",
"/app/animal-classification/INPUT_model_path/animal-cnn/savedmodel",
"/app/animal-classification/animal-cnn/savedmodel",
"animal-classification/INPUT_model_path/animal-classification/animal-cnn-savedmodel",
"animal-classification/animal-cnn-savedmodel",
"/app/animal-classification/INPUT_model_path/animal-classification/animal-cnn-savedmodel",
"/app/animal-classification/animal-cnn-savedmodel"
]
# Check for Keras format (.keras file)
keras_paths = [
"animal-classification/INPUT_model_path/animal-cnn/model.keras",
"animal-classification/animal-cnn/model.keras",
"/app/animal-classification/INPUT_model_path/animal-cnn/model.keras",
"/app/animal-classification/animal-cnn/model.keras"
]
for path in savedmodel_paths:
if os.path.exists(path):
model_path = path
model_type = 'savedmodel'
break
if not model_path:
for path in keras_paths:
if os.path.exists(path):
model_path = path
model_type = 'keras'
break
if not model_path:
# Fallback: try to find any model in the directory structure
model_base = "animal-classification"
print(f"Current working directory: {os.getcwd()}")
print(f"Files in current directory: {os.listdir('.')}")
if os.path.exists(model_base):
print(f"Contents of {model_base}:")
for root, dirs, files in os.walk(model_base):
level = root.replace(model_base, '').count(os.sep)
indent = ' ' * 2 * level
print(f"{indent}{os.path.basename(root)}/")
subindent = ' ' * 2 * (level + 1)
for file in files[:10]:
print(f"{subindent}{file}")
# Check for SavedModel directories (multiple naming conventions)
if 'savedmodel' in dirs:
model_path = os.path.join(root, 'savedmodel')
model_type = 'savedmodel'
break
if 'animal-cnn-savedmodel' in dirs:
model_path = os.path.join(root, 'animal-cnn-savedmodel')
model_type = 'savedmodel'
break
# Check if any directory contains saved_model.pb (indicating SavedModel format)
for dir_name in dirs:
potential_savedmodel = os.path.join(root, dir_name)
if os.path.exists(os.path.join(potential_savedmodel, 'saved_model.pb')):
model_path = potential_savedmodel
model_type = 'savedmodel'
break
if model_path:
break
# Check for .keras files
for file in files:
if file.endswith('.keras'):
model_path = os.path.join(root, file)
model_type = 'keras'
break
if model_path:
break
if not model_path:
raise FileNotFoundError(
f"Could not find any model (SavedModel or .keras) in {model_base}. Directory structure printed above.")
else:
raise FileNotFoundError(
f"Model directory {model_base} not found. Current directory: {os.getcwd()}, Contents: {os.listdir('.')}")
print(f"Loading model from: {model_path}")
print(f"Model type: {model_type}")
print(f"Model path exists: {os.path.exists(model_path)}")
# Load the model based on its type
try:
if model_type == 'savedmodel':
loaded_model = tf.saved_model.load(model_path)
infer = loaded_model.signatures["serving_default"]
print("SavedModel loaded successfully!")
else: # keras
# Try loading with compile=False to avoid optimizer/loss issues
try:
loaded_model = tf.keras.models.load_model(
model_path, compile=False)
print("Keras model loaded successfully (compile=False)!")
except Exception as e1:
print(f"Failed to load with compile=False: {e1}")
# Try with safe_mode if available (newer Keras versions)
try:
loaded_model = tf.keras.models.load_model(
model_path, safe_mode=False)
print("Keras model loaded successfully (safe_mode=False)!")
except Exception as e2:
print(f"Failed to load with safe_mode=False: {e2}")
raise
# For keras models, we'll use the model directly, not via signatures
infer = None
except Exception as e:
print(f"Error loading model: {e}")
import traceback
traceback.print_exc()
raise
@app.get('/health')
async def health():
return {"status": "healthy"}
@app.post('/upload/image')
async def uploadImage(img: UploadFile = File(...)):
# Image inlezen
original_image = Image.open(img.file)
resized_image = original_image.resize((64, 64))
images_to_predict = np.expand_dims(
np.array(resized_image), axis=0).astype(np.float32)
# Predict based on model type
if model_type == 'savedmodel':
# Tensor maken en infer voor SavedModel
input_tensor = tf.convert_to_tensor(images_to_predict)
result = infer(input_tensor)
predictions = list(result.values())[0].numpy()
else: # keras
# Direct prediction voor Keras model
predictions = loaded_model.predict(images_to_predict, verbose=0)
classification = predictions.argmax(axis=1)[0]
return ANIMALS[classification]
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