Upload model.py

model.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[82]:
+
+
+import numpy as np
+import tensorflow_datasets as tfds
+import tensorflow as tf
+import tensorflow_hub as hub
+import sklearn
+import random
+from glob import glob
+import matplotlib.pyplot as plt
+import requests
+
+
+# In[83]:
+
+
+print("TF version:", tf.__version__)
+print("Hub version:", hub.__version__)
+print("GPU is", "available" if tf.config.list_physical_devices('GPU') else "NOT AVAILABLE")
+
+
+# In[94]:
+
+
+
+inception_net = tf.keras.applications.EfficientNetB7()
+
+
+# In[100]:
+
+
+import requests
+
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+def classify_image(inp):
+  inp = inp.reshape((-1, 600, 600, 3))
+  inp = tf.keras.applications.efficientnet_v2.preprocess_input(inp)
+  prediction = inception_net.predict(inp).flatten()
+  confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
+  return confidences
+
+
+# In[107]:
+
+
+import gradio as gr
+title = "Classifier"
+Description = "Model,used :- Efficient Net B7,fine tuned on dataset 'https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals'"
+
+gr.Interface(fn=classify_image,
+             title = title,
+             description = Description,
+
+             inputs=gr.Image(shape=(600, 600)),
+             outputs=gr.Label(num_top_classes=3),
+             examples=["data/animals/animals/antelope/0a37838e99.jpg", "data/animals/animals/starfish/0a63e965c2.jpg"]).launch(share=True)
+