Create tb_image_processor.py

modules/tb_image_processor.py

@@ -0,0 +1,41 @@
+
+import cv2
+import numpy as np
+import logging
+from tensorflow.keras.models import load_model
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class TBImageProcessor:
+    """Processes real TB saliva images using a trained CNN model"""
+
+    def __init__(self, model_path="tb_cnn_model.h5"):
+        try:
+            self.model = load_model(model_path)
+            logger.info("TB Image Processor Model Loaded Successfully.")
+        except Exception as e:
+            logger.error(f"Failed to load TB Image Model: {e}")
+            self.model = None
+
+    def process_image(self, image_path):
+        """Analyze the TB image and return risk assessment."""
+        if not self.model:
+            return {"error": "Model not loaded. Cannot process image."}
+
+        try:
+            image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+            image = cv2.resize(image, (128, 128))  # Resizing for CNN input
+            image = np.expand_dims(image, axis=[0, -1]) / 255.0  # Normalize
+
+            prediction = self.model.predict(image)
+            confidence = float(prediction[0][0])
+            result = "TB Detected" if confidence > 0.5 else "No TB"
+            
+            return {
+                "result": result,
+                "confidence": confidence
+            }
+        except Exception as e:
+            logger.error(f"Error processing image: {e}")
+            return {"error": "Image processing failed."}