Update app.py

app.py

@@ -6,7 +6,7 @@ import tf_keras
 import tensorflow_hub as hub
 import numpy as np
 from PIL import Image
-import os
+import io
 from typing import Optional, Dict, Any, Union
 
 # Set up logging
@@ -51,22 +51,33 @@ class MedicalDiagnosisModel:
             logger.error(f"Error loading model: {str(e)}")
             return None
             
-    def preprocess_image(self, image: Image.Image):
+    def preprocess_image(self, image: np.ndarray) -> np.ndarray:
         """Preprocess the input image for model prediction."""
         try:
-            # Convert to RGB and resize
-            image = image.convert('RGB')
-            image = image.resize((256, 256))
+            logger.info(f"Input image shape: {image.shape}, dtype: {image.dtype}")
             
-            # Convert to numpy array and normalize
-            image_array = np.array(image)
-            image_array = image_array / 255.0
+            # If image is RGBA, convert to RGB
+            if image.shape[-1] == 4:
+                logger.info("Converting RGBA to RGB")
+                # Convert to PIL Image and back to handle RGBA->RGB conversion
+                image = Image.fromarray(image).convert('RGB')
+                image = np.array(image)
             
-            # Add batch dimension
-            image_array = np.expand_dims(image_array, axis=0)
-            logger.info(f"Preprocessed image shape: {image_array.shape}")
+            # Resize image
+            image = tf_keras.preprocessing.image.smart_resize(
+                image, (256, 256), interpolation='bilinear'
+            )
             
-            return image_array
+            # Ensure values are between 0 and 1
+            if image.max() > 1.0:
+                image = image / 255.0
+                
+            # Add batch dimension if not present
+            if len(image.shape) == 3:
+                image = np.expand_dims(image, axis=0)
+                
+            logger.info(f"Preprocessed image shape: {image.shape}")
+            return image
             
         except Exception as e:
             logger.error(f"Error preprocessing image: {str(e)}")
@@ -149,7 +160,7 @@ class MedicalDiagnosisApp:
         self.api = MedicalDiagnosisAPI(api_key, user_id)
         
     def process_request(self, patient_info: str, 
-                       image: Optional[Image.Image]) -> str:
+                       image: Optional[np.ndarray]) -> str:
         """Process a medical diagnosis request."""
         try:
             if self.model.model is None:
@@ -229,6 +240,7 @@ if __name__ == "__main__":
     iface = create_gradio_interface()
     iface.launch(
         server_name="0.0.0.0",
+        share=True,  # Enable public link
         debug=True
     )