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app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.models import load_model
+from tensorflow.keras.applications.efficientnet import preprocess_input
+
+# Load the trained model
+model = load_model("efficent_net224B0.h5")
+
+# Define the classes
+waste_labels = {0: 'Fibres', 1: 'Nanowires', 2: 'Particles', 3: 'Powder'}
+
+# Define the Gradio interface
+def classify_image(pil_image):
+    # Convert PIL.Image to Numpy array
+    img = image.img_to_array(pil_image)
+    
+    # Resize to the model's expected input size
+    img = tf.image.resize(img, (224, 224))
+    
+    # Expand dimensions to create a batch size of 1
+    img = np.expand_dims(img, axis=0)
+    
+    # Preprocess the input for the EfficientNet model
+    img = preprocess_input(img)
+    
+    # Make prediction
+    prediction = model.predict(img)
+    
+    # Get predicted class and confidence
+    predicted_class = np.argmax(prediction)
+    predicted_class = waste_labels[predicted_class]
+    confidence = prediction[0, np.argmax(prediction)]
+
+    return predicted_class
+
+# Create the Gradio interface
+iface = gr.Interface(fn=classify_image, inputs="image", outputs="text")
+
+# Launch the Gradio interface
+iface.launch()

requirements.txt

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+
+pandas==1.5.3
+plotly==5.0.0
+tensorflow==2.12.0
+gradio==4.7.1