Update app.py

app.py

@@ -1,103 +1,91 @@
-import os
-import io
-import base64
-from pathlib import Path
-from flask import (Flask, request, render_template,jsonify, redirect, url_for)
-from werkzeug.utils import secure_filename
-from PIL import Image
-from predict import predict_pytorch, predict_tensorflow
 
 
+import os, io, sys, base64, traceback
+from pathlib import Path
+from flask import Flask, request, render_template, jsonify
+from PIL import Image
 
-app = Flask(__name__)
+# Répertoire absolu contenant app.py
+BASE_DIR = Path(__file__).resolve().parent
+if str(BASE_DIR) not in sys.path:
+    sys.path.insert(0, str(BASE_DIR))
 
+from predict import predict_pytorch, predict_tensorflow
 
-app.config["MAX_CONTENT_LENGTH"] = 5 * 1024 * 1024   # 5 MB upload limit
-ALLOWED_EXT = {"png", "jpg", "jpeg", "webp", "bmp"}
+app = Flask(__name__, template_folder=str(BASE_DIR / "templates"))
+app.config["MAX_CONTENT_LENGTH"] = 5 * 1024 * 1024
 
-PYTORCH_MODEL_PATH = os.getenv("PYTORCH_MODEL_PATH", "sara_model.pth")
-TF_MODEL_PATH = os.getenv("TF_MODEL_PATH",      "sara_model.keras")
+ALLOWED_EXT        = {"png", "jpg", "jpeg", "webp", "bmp"}
+PYTORCH_MODEL_PATH = os.getenv("PYTORCH_MODEL_PATH", str(BASE_DIR / "sara_model.pth"))
+TF_MODEL_PATH      = os.getenv("TF_MODEL_PATH",      str(BASE_DIR / "sara_model.keras"))
 
 CLASS_ICONS = {
-    "buildings",
-    "forest",
-    "glacier",
-    "mountain",
-    "sea",
-    "street",
+    "buildings": "🏙️", "forest": "🌲", "glacier": "🧊",
+    "mountain":  "🏔️", "sea":    "🌊", "street":  "🛣️",
 }
 
+def allowed_file(filename):
+    return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXT
 
-def allowed_file(filename: str) -> bool:
-    return ("." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXT)
-
-
-def image_to_b64(img: Image.Image, fmt: str = "JPEG") -> str:
+def image_to_b64(img):
     buf = io.BytesIO()
-    img.convert("RGB").save(buf, format=fmt)
+    img.convert("RGB").save(buf, format="JPEG")
     return base64.b64encode(buf.getvalue()).decode("utf-8")
 
-
-# Routes
 @app.route("/", methods=["GET"])
 def index():
     return render_template("index.html")
 
-
 @app.route("/predict", methods=["POST"])
 def predict():
     model_choice = request.form.get("model", "pytorch")
-    file = request.files.get("image")
+    file         = request.files.get("image")
 
     if not file or file.filename == "":
-        return render_template("index.html", error="Please upload an image file."), 400
-
+        return render_template("index.html", error="Veuillez uploader une image."), 400
     if not allowed_file(file.filename):
-        return render_template("index.html", error="Unsupported file type. " "Use JPG, PNG, WEBP or BMP."), 400
+        return render_template("index.html", error="Format non supporté. Utilisez JPG, PNG, WEBP ou BMP."), 400
 
-   
     img_bytes = file.read()
-    pil_img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
-
-    tmp_path = Path("tmp_upload.jpg")
-    pil_img.save(tmp_path, format="JPEG")
+    pil_img   = Image.open(io.BytesIO(img_bytes)).convert("RGB")
+    tmp_path  = BASE_DIR / "tmp_upload.jpg"
+    pil_img.save(str(tmp_path), format="JPEG")
 
     try:
         if model_choice == "pytorch":
-            result = predict_pytorch(str(tmp_path),model_path=PYTORCH_MODEL_PATH)
+            if not Path(PYTORCH_MODEL_PATH).exists():
+                raise FileNotFoundError(f"Modèle PyTorch introuvable : {PYTORCH_MODEL_PATH}")
+            result = predict_pytorch(str(tmp_path), model_path=PYTORCH_MODEL_PATH)
         else:
-            result = predict_tensorflow(str(tmp_path),model_path=TF_MODEL_PATH)
+            if not Path(TF_MODEL_PATH).exists():
+                raise FileNotFoundError(f"Modèle TensorFlow introuvable : {TF_MODEL_PATH}")
+            result = predict_tensorflow(str(tmp_path), model_path=TF_MODEL_PATH)
+
     except FileNotFoundError as e:
         tmp_path.unlink(missing_ok=True)
-        return render_template("index.html",error=f"Model file not found: {e}. " "Train a model first."), 500
+        return render_template("index.html", error=f"Modèle introuvable : {e}"), 500
     except Exception as e:
         tmp_path.unlink(missing_ok=True)
-        return render_template("index.html",error=f"Inference error: {e}"), 500
+        print("ERREUR INFERENCE :", traceback.format_exc())
+        return render_template("index.html", error=f"Erreur : {str(e)}"), 500
     finally:
         tmp_path.unlink(missing_ok=True)
 
-    img_b64 = image_to_b64(pil_img)
-    probs = result["all_probabilities"]
-
-    # Sort by confidence descending for the bar chart
-    sorted_probs = sorted(probs.items(), key=lambda x: -x[1])
-
-    return render_template(
-        "index.html",
-        result = result,
-        model_used = model_choice,
-        img_b64 = img_b64,
-        sorted_probs = sorted_probs,
-        class_icons = CLASS_ICONS,
-    )
+    img_b64      = image_to_b64(pil_img)
+    sorted_probs = sorted(result["all_probabilities"].items(), key=lambda x: -x[1])
 
+    return render_template("index.html",
+        result=result, model_used=model_choice,
+        img_b64=img_b64, sorted_probs=sorted_probs, class_icons=CLASS_ICONS)
 
 @app.route("/health")
 def health():
-    return jsonify({"status": "ok"}), 200
-
-
+    return jsonify({"status": "ok", "pytorch": Path(PYTORCH_MODEL_PATH).exists(),
+                    "tensorflow": Path(TF_MODEL_PATH).exists()}), 200
 
 if __name__ == "__main__":
     port = int(os.getenv("PORT", 7860))
+    print(f"BASE_DIR : {BASE_DIR}")
+    print(f"PyTorch  : {PYTORCH_MODEL_PATH} — existe : {Path(PYTORCH_MODEL_PATH).exists()}")
+    print(f"TF       : {TF_MODEL_PATH} — existe : {Path(TF_MODEL_PATH).exists()}")
     app.run(host="0.0.0.0", port=port, debug=False)