Update app.py

app.py

@@ -4,6 +4,7 @@ from torch import nn
 from torchvision import models, transforms
 from huggingface_hub import hf_hub_download
 from PIL import Image
+from flask import Flask, request, jsonify
 
 # Define the number of classes
 num_classes = 2  # Update with the actual number of classes in your dataset (e.g., 2 for healthy and anomalous)
@@ -62,4 +63,28 @@ iface = gr.Interface(
 )
 
 # Launch the Gradio interface
-iface.launch()
+iface.launch(share=True)
+
+# Create a Flask app
+app = Flask(__name__)
+
+# Define the API endpoint
+@app.route('/predict', methods=['POST'])
+def api_predict():
+    try:
+        data = request.json
+        image_path = data['inputs']
+        
+        # Load the image
+        image = Image.open(image_path)
+        
+        # Perform prediction
+        result = predict(image)
+        
+        return jsonify({"result": result})
+    except Exception as e:
+        return jsonify({"error": str(e)}), 400
+
+# Run the Flask app
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=8000)