Upload 3 files

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from keras.applications.imagenet_utils import preprocess_input
+from tensorflow.keras.preprocessing import image
+import requests
+from PIL import Image
+import io
+
+model = load_model('api\\vgg_model.h5')
+
+def infer(image_url):
+    # Load and preprocess the image
+    response = requests.get(image_url)
+    image = Image.open(io.BytesIO(response.content))
+    image = image.resize((224, 224))  # Resize image to match input size of VGG16
+    x = image
+    x = np.expand_dims(x, axis=0)
+    x = preprocess_input(x)
+    
+    # Perform inference
+    preds = model.predict(x)
+    result = preds[0][0]
+
+    # Determine the label
+    if result < preds[0][1]:
+        label = "messy"
+    else:
+        label = "clean"
+        
+    return label
+
+# Create a Gradio interface
+iface = gr.Interface(fn=infer, inputs="text", outputs="text", title="Image Classifier")
+iface.launch()

requirements.txt

@@ -0,0 +1,9 @@
+gradio
+numpy
+tensorflow
+keras
+fastapi
+requests
+Pillow
+uvicorn
+io

vgg_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:974296d00fb53d5dd635fe84006117cf57a2837b2119eb2d5b71b1dae3f83997
+size 62111032