Update app.py

app.py

@@ -13,8 +13,8 @@ classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnl
 image_model = timm.create_model('resnet50', pretrained=True)
 image_model.eval()
 
-# Load saved TensorFlow eye disease detection model
-eye_model = tf.keras.models.load_model('model.h5')
+# Load saved TensorFlow eye disease detection model (TensorFlow Model without Keras)
+eye_model = tf.saved_model.load('model')
 
 # Patient database
 patients_db = []
@@ -87,9 +87,9 @@ def extract_pdf_report(pdf):
 def predict_eye_disease(input_image):
     input_image = tf.image.resize(input_image, [224, 224]) / 255.0
     input_image = tf.expand_dims(input_image, 0)
-    predictions = eye_model.predict(input_image)
+    predictions = eye_model(input_image)
     labels = ['Cataract', 'Conjunctivitis', 'Glaucoma', 'Normal']
-    confidence_scores = {labels[i]: round(predictions[0][i] * 100, 2) for i in range(len(labels))}
+    confidence_scores = {labels[i]: round(predictions[i] * 100, 2) for i in range(len(labels))}
     if confidence_scores['Normal'] > 50:
         return f"Congrats! No disease detected. Confidence: {confidence_scores['Normal']}%"
     return "\n".join([f"{label}: {confidence}%" for label, confidence in confidence_scores.items()])