Update app.py

app.py

@@ -7,40 +7,68 @@ from werkzeug.utils import secure_filename
 
 app = Flask(__name__)
 
-# Load trained model
-model = tf.keras.models.load_model("trained.h5")
+# Ensure the uploads directory exists
+UPLOAD_FOLDER = "uploads"
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER
 
-# Define allowed extensions
+# Allowed file types
 ALLOWED_EXTENSIONS = {"png", "jpg", "jpeg"}
 
 def allowed_file(filename):
-    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+    return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS
+
+# Load trained model
+try:
+    model = tf.keras.models.load_model("trained.h5")
+    print("✅ Model loaded successfully")
+except Exception as e:
+    print(f"❌ Error loading model: {str(e)}")
+    model = None
 
 def preprocess_image(img_path):
-    img = image.load_img(img_path, target_size=(150, 150))  # Resize to model input size
-    img_array = image.img_to_array(img) / 255.0  # Normalize
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
-    return img_array
+    """ Preprocesses image for model prediction. """
+    try:
+        img = image.load_img(img_path, target_size=(150, 150))  # Resize
+        img_array = image.img_to_array(img) / 255.0  # Normalize
+        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+        return img_array
+    except Exception as e:
+        print(f"❌ Error in image preprocessing: {str(e)}")
+        return None
 
 @app.route("/predict", methods=["POST"])
 def predict():
+    """ Handles image upload and model prediction. """
+    if model is None:
+        return jsonify({"error": "Model not loaded. Check logs for details."}), 500
+
     if "file" not in request.files:
         return jsonify({"error": "No file uploaded"}), 400
 
     file = request.files["file"]
-    
+
     if file.filename == "":
         return jsonify({"error": "No selected file"}), 400
 
     if file and allowed_file(file.filename):
         filename = secure_filename(file.filename)
-        file_path = os.path.join("uploads", filename)
+        file_path = os.path.join(app.config["UPLOAD_FOLDER"], filename)
         file.save(file_path)
 
         img_array = preprocess_image(file_path)
-        prediction = model.predict(img_array)[0][0]
+        if img_array is None:
+            os.remove(file_path)
+            return jsonify({"error": "Invalid image format"}), 400
+
+        try:
+            prediction = model.predict(img_array)[0][0]
+        except Exception as e:
+            os.remove(file_path)
+            print(f"❌ Prediction Error: {str(e)}")
+            return jsonify({"error": "Error making prediction"}), 500
 
-        os.remove(file_path)  # Clean up
+        os.remove(file_path)  # Cleanup
 
         result = "Pneumonia Detected" if prediction > 0.5 else "No Pneumonia"
         confidence = float(prediction) if prediction > 0.5 else 1 - float(prediction)
@@ -50,4 +78,4 @@ def predict():
     return jsonify({"error": "Invalid file format"}), 400
 
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+    app.run(host="0.0.0.0", port=7860, debug=True, threaded=True)