Update app.py

app.py

@@ -1,10 +1,11 @@
 import streamlit as st
 import tensorflow as tf
+from tensorflow.keras.layers import InputLayer
 from PIL import Image
 import numpy as np
 # from .modules.tools import Analysing_image, text_to_speech
 from gtts import gTTS
-from transformers import BioGptTokenizer, AutoModelForCausalLM,  pipeline
+from transformers import BioGptTokenizer, AutoModelForCausalLM,  pipeline, TFAutoModel
 from deep_translator import GoogleTranslator
 import tempfile
 import os
@@ -101,7 +102,17 @@ with tab1:
             st.info("Please upload an image to classify.")
     else:
         st.info("Using Pretrained model")
-        model = tf.keras.models.load_model("./models/medical_classifier.h5")
+        # model = tf.keras.models.load_model("./models/medical_classifier.h5")
+        model = TFAutoModel.from_pretrained('./models/medical_classifier.h5')
+        config = model.config
+        if 'batch_shape' in config:
+            config['input_shape'] = config.pop('batch_shape')[1:]  # Remove batch size
+            
+        # Create the input layer with the corrected configuration
+        input_layer = InputLayer(**model.config)
+        # Rebuild the model (if necessary)
+        model.build(input_shape=model.config['input_shape'])
+
         if image_file is not None:
             predict_class = Analysing_image(st, model, image_file)
         else: