Update app.py

app.py

@@ -2,8 +2,6 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 from PIL import Image
-import requests
-from io import BytesIO
 
 # Load the model
 model = tf.keras.models.load_model('Nutrient-Model.h5')
@@ -11,6 +9,7 @@ model = tf.keras.models.load_model('Nutrient-Model.h5')
 # Define the class names
 class_names = ['Iron', 'Magnesium', 'Nitrogen', 'Potassium', 'Zinc']
 
+# Function to classify the image
 def classify_image(image):
     # Convert the numpy array to a PIL Image object
     pil_image = Image.fromarray(np.uint8(image)).convert('RGB')
@@ -40,9 +39,9 @@ def classify_image(image):
     return f"{predicted_class} ({confidence_score*100:.2f}%)"
 
 # Define the Gradio interface
-image_input = gr.inputs.Image()
-output_text = gr.outputs.Textbox()
-gr.Interface(fn=classify_image, inputs=image_input, outputs=output_text, title="Image Classification", description="Classify an image into one of five classes: Iron, Magnesium, Nitrogen, Potassium, or Zinc."
-).launch(share=True)
-
+inputs = gr.inputs.Image()
+outputs = gr.outputs.Textbox()
+interface = gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, title="Image Classification", description="Classify an image into one of five classes: Iron, Magnesium, Nitrogen, Potassium, or Zinc.")
 
+# Launch the interface
+interface.launch(share=True)