Update app.py

app.py

@@ -1,12 +1,12 @@
 import gradio as gr
 import tensorflow as tf
 from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
 import numpy as np
 import os
-from huggingface_hub import hf_hub_download
+import requests
 
-# Labels for class and species
+# Load your model and tokenizer
 labels = {
     'class': ['amphibia', 'aves', 'invertebrates', 'lacertilia', 'mammalia', 'serpentes', 'testudines'],
     'serpentes': ["Butler's Gartersnake", "Dekay's Brownsnake", 'Eastern Gartersnake', 'Eastern Hog-nosed snake', 'Eastern Massasauga', 'Eastern Milksnake', 'Eastern Racer Snake', 'Eastern Ribbonsnake', 'Gray Ratsnake', "Kirtland's Snake", 'Northern Watersnake', 'Plains Gartersnake', 'Red-bellied Snake', 'Smooth Greensnake'],
@@ -15,16 +15,13 @@ labels = {
     'amphibia': ['American Bullfrog', 'American Toad', 'Green Frog', 'Northern Leopard Frog']
 }
 
-# Function to download the model from Hugging Face Hub
-def get_model_from_hub(repo_id, filename):
-    model_path = hf_hub_download(repo_id=repo_id, filename=filename)
-    return load_model(model_path)
-
-# Download and load the model
-hierarchical_models = {
-    'class': get_model_from_hub("sowbaranika13/inceptionv3_class", "inceptionv3_class.h5")
-}
+hierarchical_models = {}
 
+model_path = r"inceptionv3_class.h5"
+print(model_path)
+hierarchical_models['class'] = load_model(model_path)
+print(hierarchical_models)
+    
 def load_and_preprocess_image(image, target_size=(299, 299)):
     img_array = img_to_array(image)
     img_array = np.expand_dims(img_array, axis=0)
@@ -46,7 +43,7 @@ def predict(image):
     species_idx = np.argmax(species_preds)
     species_label = labels[class_label][species_idx]
     results['species'] = species_label
-    
+    print(results)
     return results
 
 # Create Gradio interface
@@ -59,4 +56,4 @@ iface = gr.Interface(
 )
 
 # Launch the interface
-iface.launch()
+iface.launch()