Upload app.py

app.py

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+from transformers import pipeline
+import onnxruntime
+import numpy as np
+from PIL import Image
+import gradio as gr
+
+# Load the ONNX model
+onnx_model_path = "https://huggingface.co/spaces/Anas090/sites_classification/resolve/main/InceptionV3-20epochs.onnx?dl=1"
+session = onnxruntime.InferenceSession(onnx_model_path)
+
+class_labels = ['Ajloun Castle', 'Hadrians Arch', 'Petra-siq', 'Roman Ruins-Jerash', 'Roman amphitheater', 'The Cardo Maximus of Jerash', 'Wadi Rum', 'petra-Treasury', 'umm qais']
+
+dic ={'Ajloun Castle': 0,
+ 'Hadrians Arch': 1,
+ 'Petra-siq': 2,
+ 'Roman Ruins-Jerash': 3,
+ 'Roman amphitheater': 4,
+ 'The Cardo Maximus of Jerash': 5,
+ 'Wadi Rum': 6,
+ 'petra-Treasury': 7,
+ 'umm qais': 8}
+
+def classify_image(image, labels_text, model_name, hypothesis_template):
+    img = Image.open(image).resize((475, 550))
+    img_array = np.array(img).astype(np.float32) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Run inference with the ONNX model
+    output = session.run(None, {"input": img_array})  # Replace "input" with the actual input name of your ONNX model
+
+    # Get the predicted class index
+    predicted_class_index = np.argmax(output)
+
+    # Map the class index to the corresponding label
+    predicted_class = class_labels[predicted_class_index]
+
+    return {predicted_class: 1.0}  # You may need to adjust the confidence score based on your model's output
+
+inputs = [
+    gr.inputs.Image(type='pil', label="Site_image"),
+    gr.inputs.Radio(choices=[
+        "ViT/B-16",
+        "ViT/L-14",
+        "ViT/L-14@336px",
+        "ViT/H-14",
+    ], type="value", default="ViT/B-16", label="Model 模型规模"),
+]
+
+iface = gr.Interface(classify_image,
+                     inputs,
+                     "label",
+                     title="Your Title Here")
+iface.launch()