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Intel Scene Classifier — Flask App
"""
import io
import os
import numpy as np
from flask import Flask, jsonify, render_template, request
from PIL import Image
app = Flask(__name__)
CLASSES = ["buildings", "forest", "glacier", "mountain", "sea", "street"]
IMG_SIZE = 150
_pytorch_model = None
_tf_model = None
# ── Loaders ────────────────────────────────────────────────────────────────────
def load_pytorch():
global _pytorch_model
if _pytorch_model is not None:
return _pytorch_model
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
class CNN_Torch(nn.Module):
def __init__(self, num_classes=6):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.conv3_drop = nn.Dropout2d(p=0.25)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(128 * 18 * 18, 256)
self.fc2 = nn.Linear(256, num_classes)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.pool(F.relu(self.conv3_drop(self.conv3(x))))
x = x.view(-1, 128 * 18 * 18)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = CNN_Torch(6).to(device)
model.load_state_dict(torch.load("parfait_model.pth", map_location=device))
model.eval()
tf = transforms.Compose([
transforms.Resize((IMG_SIZE, IMG_SIZE)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
_pytorch_model = (model, device, tf)
return _pytorch_model
def load_tensorflow():
global _tf_model
if _tf_model is None:
import tensorflow as tf
_tf_model = tf.keras.models.load_model("parfait_model.keras")
return _tf_model
# ── Routes ─────────────────────────────────────────────────────────────────────
@app.route("/")
def index():
return render_template("index.html")
@app.route("/predict", methods=["POST"])
def predict():
if "image" not in request.files:
return jsonify({"error": "Aucune image fournie"}), 400
framework = request.form.get("model", "pytorch")
try:
pil_img = Image.open(io.BytesIO(request.files["image"].read())).convert("RGB")
except Exception:
return jsonify({"error": "Fichier image invalide"}), 400
try:
if framework == "pytorch":
import torch
model, device, tf = load_pytorch()
tensor = tf(pil_img).unsqueeze(0).to(device)
with torch.no_grad():
out = model(tensor)
probs = torch.exp(out).cpu().numpy()[0]
else:
model = load_tensorflow()
arr = np.array(pil_img.resize((IMG_SIZE, IMG_SIZE)), dtype=np.float32)
probs = model.predict(np.expand_dims(arr, 0), verbose=0)[0]
pred_idx = int(np.argmax(probs))
return jsonify({
"class": CLASSES[pred_idx],
"confidence": float(probs[pred_idx]),
"probabilities": {c: float(p) for c, p in zip(CLASSES, probs)},
})
except FileNotFoundError as e:
return jsonify({"error": f"Modèle introuvable : {e}. Placez les fichiers .pth et .keras à la racine."}), 500
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == "__main__":
port = int(os.environ.get("PORT", 5000))
app.run(host="0.0.0.0", port=port, debug=False)
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