app.py

# ============================================================
#  APPLICATION FLASK - CLASSIFICATION D'IMAGES
# ============================================================
from flask import Flask, render_template, request
import torch
import torch.nn as nn
import torchvision.transforms as T
import tensorflow as tf
from PIL import Image
import numpy as np
import io
import os

app = Flask(__name__)

CLASSES = ['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street']
IMG_SIZE = 150
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
# ── Définition du CNN PyTorch (même architecture) ────────────
class CNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1), nn.BatchNorm2d(32), nn.ReLU(), nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 3, padding=1), nn.BatchNorm2d(64), nn.ReLU(), nn.MaxPool2d(2),
            nn.Conv2d(64, 128, 3, padding=1), nn.BatchNorm2d(128), nn.ReLU(), nn.MaxPool2d(2),
        )
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(128 * 18 * 18, 256), nn.ReLU(), nn.Dropout(0.5),
            nn.Linear(256, 6),
        )
    def forward(self, x):
        return self.classifier(self.features(x))

# ── Chargement des modèles ───────────────────────────────────
model_pytorch = CNN()
model_pytorch.load_state_dict(
    torch.load(
        os.path.join(BASE_DIR, "models", "binta_py_model.pth"),
        map_location="cpu"
    )
)
model_pytorch.eval()
 
model_tensorflow = tf.keras.models.load_model(
    os.path.join(BASE_DIR, "models", "binta_ten_model.keras"),
    compile=False
)
# ── Transform pour PyTorch ───────────────────────────────────
transform = T.Compose([
    T.Resize((IMG_SIZE, IMG_SIZE)),
    T.ToTensor(),
    T.Normalize(mean=[0.485, 0.456, 0.406],
                std =[0.229, 0.224, 0.225]),
])

# ── Fonction de prédiction ───────────────────────────────────
def predict(image, model_choice):
    img = Image.open(io.BytesIO(image)).convert("RGB")

    if model_choice == "pytorch":
        tensor = transform(img).unsqueeze(0)   # ajoute dimension batch
        with torch.no_grad():
            out = model_pytorch(tensor)
        return CLASSES[out.argmax(1).item()]

    elif model_choice == "tensorflow":
        img = img.resize((IMG_SIZE, IMG_SIZE))
        arr = np.array(img) / 255.0
        arr = np.expand_dims(arr, axis=0)      # ajoute dimension batch
        out = model_tensorflow.predict(arr)
        return CLASSES[np.argmax(out)]

# ── Routes ───────────────────────────────────────────────────
@app.route("/", methods=["GET", "POST"])
def index():
    prediction = None
    model_choice = None
    if request.method == "POST":
        model_choice = request.form["model"]
        image_file   = request.files["image"].read()
        prediction   = predict(image_file, model_choice)
    return render_template("index.html",
                           prediction=prediction,
                           model_choice=model_choice)

if __name__ == "__main__":
    app.run(host='0.0.0.0', port=7860)