Update app.py

app.py

@@ -4,7 +4,6 @@ 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)
@@ -62,29 +61,5 @@ iface = gr.Interface(
     description="Upload an image of wheat to detect anomalies like disease or pest infestation."
 )
 
-# Launch the Gradio interface on a different port
-iface.launch(share=True, server_port=7861)
-
-# 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 on a different port
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=8001)
+# Launch the Gradio interface
+iface.launch()