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| from flask import Flask, request, jsonify | |
| import tensorflow as tf | |
| import numpy as np | |
| from tensorflow.keras.preprocessing import image | |
| import os | |
| import logging | |
| from werkzeug.utils import secure_filename | |
| # Initialize Flask app | |
| app = Flask(__name__) | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") | |
| # Set up uploads folder | |
| UPLOAD_FOLDER = "uploads" | |
| if not os.path.exists(UPLOAD_FOLDER): | |
| try: | |
| os.makedirs(UPLOAD_FOLDER, exist_ok=True) | |
| os.chmod(UPLOAD_FOLDER, 0o755) # Set proper permissions | |
| except Exception as e: | |
| logging.error(f"❌ Error creating uploads directory: {e}") | |
| app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER | |
| # Allowed file types | |
| ALLOWED_EXTENSIONS = {"png", "jpg", "jpeg"} | |
| def allowed_file(filename): | |
| """Check if file has an allowed extension.""" | |
| return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS | |
| # Load trained model | |
| try: | |
| model = tf.keras.models.load_model("trained.h5") | |
| logging.info("✅ Model loaded successfully.") | |
| except Exception as e: | |
| logging.error(f"❌ Error loading model: {e}") | |
| model = None | |
| def preprocess_image(img_path): | |
| """Preprocesses the image for model prediction.""" | |
| try: | |
| 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 | |
| except Exception as e: | |
| logging.error(f"❌ Error in image preprocessing: {e}") | |
| return None | |
| def predict(): | |
| """Handles image upload and model prediction.""" | |
| if model is None: | |
| return jsonify({"error": "Model not loaded. Check server 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(app.config["UPLOAD_FOLDER"], filename) | |
| file.save(file_path) | |
| img_array = preprocess_image(file_path) | |
| 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) | |
| logging.error(f"❌ Prediction Error: {e}") | |
| return jsonify({"error": "Error making prediction"}), 500 | |
| 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) | |
| return jsonify({"result": result, "confidence": round(confidence * 100, 2)}) | |
| return jsonify({"error": "Invalid file format"}), 400 | |
| if __name__ == "__main__": | |
| app.run(host="0.0.0.0", port=7860, debug=True, threaded=True) | |