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.gitattributes

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README.md

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+---
+title: Shark Classifier Mobilenet_v2
+emoji: 🦈
+colorFrom: indigo
+colorTo: green
+sdk: gradio
+app_file: app.py
+pinned: false
+---
+
+# Configuration
+
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`sdk_version` : _string_  
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+from tensorflow.keras import models 
+
+import tensorflow as tf
+
+# open categories.txt in read mode
+categories = open("categories.txt", "r")
+labels = categories.readline().split(";")
+
+model = models.load_model('models/modelnet/best_model.h5')
+
+
+def predict_image(image):
+    image = np.array(image) / 255
+    image = np.expand_dims(image, axis=0)
+
+    pred = model.predict(image)
+
+    acc = dict((labels[i], "%.2f" % pred[0][i]) for i in range(len(labels)))
+    print(acc)
+    return acc
+
+
+image = gr.inputs.Image(shape=(224, 224), label="Upload Your Image Here")
+label = gr.outputs.Label(num_top_classes=len(labels))
+
+samples = ['samples/basking.jpg', 'samples/blacktip.jpg', 'samples/blue.jpg', 'samples/bull.jpg', 'samples/hammerhead.jpg',
+        'samples/lemon.jpg', 'samples/mako.jpg', 'samples/nurse.jpg', 'samples/sand tiger.jpg', 'samples/thresher.jpg', 
+        'samples/tigre.jpg', 'samples/whale.jpg', 'samples/white.jpg', 'samples/whitetip.jpg']
+        
+interface = gr.Interface(
+    fn=predict_image, 
+    inputs=image, 
+    outputs=label, 
+    capture_session=True, 
+    allow_flagging=False, 
+    examples=samples
+)
+interface.launch()

categories.txt

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+basking;blacktip;blue;bull;hammerhead;lemon;mako;nurse;sand tiger;thresher;tiger;whale;white;whitetip

requirements.txt

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+gradio
+torch
+torchvision
+Pillow
+gdown
+numpy
+scipy
+cmake
+onnxruntime-gpu
+opencv-python-headless
+encoded-video
+hugsvision
+tensorflow