Update app.py

app.py

@@ -14,7 +14,7 @@ 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')
+#eye_model = tf.keras.models.load_model('model.h5')
 
 # Patient database
 patients_db = []
@@ -214,7 +214,7 @@ def extract_pdf_report(pdf):
             text += page.extract_text()
     return text
 
-def predict_eye_disease(input_image):
+'''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)
@@ -223,7 +223,7 @@ def predict_eye_disease(input_image):
     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()])
-
+'''
 def doctor_space(patient_id):
     for patient in patients_db:
         if patient["ID"] == patient_id:
@@ -287,12 +287,12 @@ report_analysis_interface = gr.Interface(
     outputs="text",
 )
 
-eye_disease_interface = gr.Interface(
+'''eye_disease_interface = gr.Interface(
     fn=predict_eye_disease,
     inputs=gr.Image(label="Upload an Eye Image", type="numpy"),
     outputs="text",
 )
-
+'''
 doctor_space_interface = gr.Interface(
     fn=doctor_space,
     inputs=gr.Number(label="Patient ID"),
@@ -333,9 +333,9 @@ with gr.Blocks() as app:
     with gr.Tab("Extract PDF Report"):
         pdf_extraction_interface.render()
     
-    with gr.Tab("Ophthalmologist Space"):
+'''    with gr.Tab("Ophthalmologist Space"):
         eye_disease_interface.render()
-    
+   ''' 
     with gr.Tab("Doctor Space"):
         doctor_space_interface.render()