Spaces:
Sleeping
Sleeping
File size: 8,163 Bytes
3343c55 66d71ff 3343c55 8db4042 3343c55 66d71ff 3343c55 8db4042 3343c55 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 |
from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import HTMLResponse
import numpy as np
from PIL import Image
from tensorflow.keras.models import load_model
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
ANIMALS = ['Cat', 'Dog', 'Panda'] # Animal names here, these represent the labels of the images that we trained our model on.
@app.get("/", response_class=HTMLResponse)
def test_upload():
html_content = """
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Animal Image Classifier</title>
<style>
body {
font-family: Arial, sans-serif;
max-width: 800px;
margin: 50px auto;
padding: 20px;
background-color: #f5f5f5;
}
.container {
background-color: white;
padding: 30px;
border-radius: 10px;
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
}
h1 {
color: #333;
text-align: center;
}
.upload-section {
margin: 20px 0;
padding: 20px;
border: 2px dashed #ccc;
border-radius: 5px;
text-align: center;
}
input[type="file"] {
margin: 10px 0;
}
button {
background-color: #4CAF50;
color: white;
padding: 10px 20px;
border: none;
border-radius: 5px;
cursor: pointer;
font-size: 16px;
}
button:hover {
background-color: #45a049;
}
button:disabled {
background-color: #cccccc;
cursor: not-allowed;
}
.preview {
margin: 20px 0;
text-align: center;
}
.preview img {
max-width: 300px;
max-height: 300px;
border-radius: 5px;
box-shadow: 0 2px 5px rgba(0,0,0,0.2);
}
.result {
margin-top: 20px;
padding: 20px;
background-color: #e8f5e9;
border-radius: 5px;
text-align: center;
font-size: 20px;
font-weight: bold;
color: #2e7d32;
}
.error {
background-color: #ffebee;
color: #c62828;
}
.loading {
display: none;
margin: 20px 0;
text-align: center;
}
.spinner {
border: 4px solid #f3f3f3;
border-top: 4px solid #4CAF50;
border-radius: 50%;
width: 40px;
height: 40px;
animation: spin 1s linear infinite;
margin: 0 auto;
}
@keyframes spin {
0% { transform: rotate(0deg); }
100% { transform: rotate(360deg); }
}
</style>
</head>
<body>
<div class="container">
<h1>🐾 Animal Image Classifier</h1>
<p style="text-align: center; color: #666;">Upload an image of a Cat, Dog, or Panda to classify it!</p>
<div class="upload-section">
<input type="file" id="imageInput" accept="image/*">
<br>
<button onclick="uploadImage()" id="uploadBtn">Classify Image</button>
</div>
<div class="loading" id="loading">
<div class="spinner"></div>
<p>Classifying...</p>
</div>
<div class="preview" id="preview"></div>
<div id="result"></div>
</div>
<script>
let selectedFile = null;
document.getElementById('imageInput').addEventListener('change', function(e) {
const file = e.target.files[0];
if (file) {
selectedFile = file;
// Show preview
const reader = new FileReader();
reader.onload = function(e) {
document.getElementById('preview').innerHTML =
'<img src="' + e.target.result + '" alt="Preview">';
}
reader.readAsDataURL(file);
document.getElementById('result').innerHTML = '';
}
});
async function uploadImage() {
if (!selectedFile) {
alert('Please select an image first!');
return;
}
const uploadBtn = document.getElementById('uploadBtn');
const loading = document.getElementById('loading');
const resultDiv = document.getElementById('result');
// Show loading, disable button
uploadBtn.disabled = true;
loading.style.display = 'block';
resultDiv.innerHTML = '';
const formData = new FormData();
formData.append('img', selectedFile);
try {
const response = await fetch('/upload/image', {
method: 'POST',
body: formData
});
if (response.ok) {
const result = await response.text();
const animal = result.replace(/"/g, ''); // Remove quotes if present
// Display result with emoji
const emojis = {
'Cat': '🐱',
'Dog': '🐶',
'Panda': '🐼'
};
resultDiv.innerHTML =
'<div class="result">Prediction: ' +
(emojis[animal] || '') + ' ' + animal + '</div>';
} else {
resultDiv.innerHTML =
'<div class="result error">Error: ' + response.status + '</div>';
}
} catch (error) {
resultDiv.innerHTML =
'<div class="result error">Error: ' + error.message + '</div>';
} finally {
// Hide loading, enable button
loading.style.display = 'none';
uploadBtn.disabled = false;
}
}
</script>
</body>
</html>
"""
return HTMLResponse(content=html_content)
model = load_model("hf://nathansegers/masterclass-2025")
@app.post('/upload/image')
async def uploadImage(img: UploadFile = File(...)):
original_image = Image.open(img.file) # Read the bytes and process as an image
resized_image = original_image.resize((64, 64)) # Resize
images_to_predict = np.expand_dims(np.array(resized_image), axis=0) # Our AI Model wanted a list of images, but we only have one, so we expand it's dimension
predictions = model.predict(images_to_predict) # The result will be a list with predictions in the one-hot encoded format: [ [0 1 0] ]
prediction_probabilities = predictions
classifications = prediction_probabilities.argmax(axis=1) # We try to fetch the index of the highest value in this list [ [1] ]
return ANIMALS[classifications.tolist()[0]] # Fetch the first item in our classifications array, format it as a list first, result will be e.g.: "Dog"
|