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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+transferlearning_tools.keras filter=lfs diff=lfs merge=lfs -text

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+model_path = "transferlearning_tools.keras"
+model = tf.keras.models.load_model(model_path)
+ 
+# Define the core prediction function
+def predict_tools(image):
+    # Preprocess image
+    print(type(image))
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150))  # Resize the image to 150x150
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)  # Expand dimensions to match the model input shape
+    
+    # Predict
+    prediction = model.predict(image)
+    
+    # Print the shape of the prediction to debug
+    print(f"Prediction shape: {prediction.shape}")
+    
+    # Assuming the output is already softmax probabilities
+    probabilities = prediction[0]
+    
+    # Print the probabilities array to debug
+    print(f"Probabilities: {probabilities}")
+    
+    # Assuming your model was trained with these class names
+    class_names = ['Gasoline can', 'Hammer', 'Rope', 'Screw driver', 'Wrench'] 
+    
+    # Create a dictionary of class probabilities
+    result = {class_names[i]: float(probabilities[i]) for i in range(len(class_names))}
+    
+    return result
+ 
+
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_tools,
+    inputs=input_image, 
+    outputs=gr.Label(),
+    examples=["tools_examples/Gasoline can 1.jpg", 
+              "tools_examples/Gasoline can 2.jpg", 
+              "tools_examples/Hammer 1.jpg", 
+              "tools_examples/Hammer 2.jpg", 
+              "tools_examples/Rope 1.jpg", 
+              "tools_examples/Rope 2.jpg", 
+              "tools_examples/Screw driver 1.jpg", 
+              "tools_examples/Screw driver 2.jpg", 
+              "tools_examples/Wrench 1.jpg", 
+              "tools_examples/Wrench 2.jpg"],   
+    description="A simple mlp classification model for image classification using the mnist dataset.")
+iface.launch()

requirements.txt

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+tensorflow

transferlearning_tools.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:302827d25c9e7f8a07f4c1a40af2c4f939a72291f3794b60b726b6b73d8bb09b
+size 250609445