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app.py

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+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')
+
+# Define the class names
+class_names = ['Iron', 'Magnesium', 'Nitrogen', 'Potassium', 'Zinc']
+
+def classify_image(image):
+    # Convert the numpy array to a PIL Image object
+    pil_image = Image.fromarray(np.uint8(image)).convert('RGB')
+
+    # Resize the image
+    pil_image = pil_image.resize((224, 224))
+
+    # Convert the PIL Image object to a numpy array
+    image_array = np.array(pil_image)
+
+    # Normalize the image
+    normalized_image_array = (image_array.astype(np.float32) / 255.0)
+
+    # Reshape the image
+    data = normalized_image_array.reshape((1, 224, 224, 3))
+
+    # Make the prediction
+    prediction = model.predict(data)[0]
+
+    # Get the predicted class name
+    predicted_class = class_names[np.argmax(prediction)]
+
+    # Get the confidence score for the predicted class
+    confidence_score = np.max(prediction)
+
+    # Return the predicted class and confidence score
+    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)
+
+

requirements.txt

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+gradio==3.28.1
+numpy==1.23.4
+Pillow==9.1.1
+Pillow==9.5.0
+Requests==2.29.0
+tensorflow==2.10.0
+tensorflow_gpu==2.10.1