update for newer versions of gradio and the update model

app.py

@@ -2,21 +2,28 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 import requests
+from huggingface_hub import snapshot_download
 
-labels = ["missing"] * 36
+label_count = 51
+labels = ["missing"] * label_count
 with open('labels.txt','r') as f:
   labels = f.read().splitlines()
 
+model_path = extractor_path = snapshot_download(repo_id="eshieh2/jaguarid_pantanal")
+model = tf.saved_model.load(f"{model_path}/saved_model")
+serving = model.signatures['serving_default']
+#model = tf.keras.models.load_model(f"{model_path}/saved_model")
 def classify_image(inp):
-  model = tf.keras.models.load_model('saved_model')
   inp = inp.resize((480,480))
   inp = np.array(inp)
   inp = np.reshape(inp,(-1, 480, 480, 3)).astype(np.float32)
   inp = np.divide(inp,255.0)
-  prediction = model.predict(inp).flatten()
-  return {labels[i]: float(prediction[i]) for i in range(36)}
+  #prediction = model.predict(inp).flatten()
+  prediction = serving(tf.convert_to_tensor(inp))['model']
+  prediction = tf.squeeze(prediction)
+  return {labels[i]: float(prediction[i]) for i in range(label_count)}
 
-image = gr.inputs.Image(type='pil')
-label = gr.outputs.Label(num_top_classes=3)
+image = gr.Image(type='pil')
+label = gr.Label(num_top_classes=3)
 
-gr.Interface(fn=classify_image, inputs=image, outputs=label, capture_session=True, theme = "grass", examples = [["test.jpg"]]).launch()
+gr.Interface(fn=classify_image, inputs=image, outputs=label, examples = [["kasimir.jpg","guaraci.jpg"]]).launch()

labels.txt

@@ -9,7 +9,7 @@ Courtney
 Donal
 Estella
 Forasteiro
-Guarassi
+Guaraci
 Hero
 Ibaca
 Inca
@@ -33,4 +33,19 @@ Tomas
 Tusk
 Xando
 Xingu
-unknown
+negative
+Antares
+Carvarinha
+Kyyaverá
+Ousado
+aju
+brady
+branco
+buraca
+xingua
+alira
+chevo
+acerola
+nusa
+surya
+