Update app.py

app.py

@@ -1,30 +1,51 @@
 import tensorflow as tf
 import gradio as gr
 import numpy as np
+import os
+from PIL import Image
+
+print(tf.__version__)
+
+print(f"Current working directory: {os.getcwd()}")
+print(f"Contents of model directory: {os.listdir('model')}")
+
+model_path = 'model/transferlearning_pokemon.h5'
+
+# Check if the model exists
+if os.path.exists(model_path):
+    print(f"Model found at {model_path}")
+    try:
+        # Load the trained model
+        model = tf.keras.models.load_model(model_path)
+        print("Model loaded successfully.")
+    except Exception as e:
+        print(f"Error loading model: {e}")
+else:
+    print(f"Model not found at {model_path}. Please check the path.")
 
-# Load the trained model
-model = tf.keras.models.load_model('transferlearning_pokemon.keras')
 
 # Define class names (make sure this matches the classes used during training)
-class_names = ['Raichu', 'Vulpix', 'Machamp'] 
+class_names = ['Machamp', 'Raichu', 'Vulpix']
 
 # Define the prediction function
 def predict(image):
-    image = tf.image.resize(image, (150, 150))  # Resize image to match model's input size
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0) # same as image[None, ...]
     predictions = model.predict(image)
     predicted_class = np.argmax(predictions, axis=1)[0]
     confidence = np.max(predictions)
     return {class_names[predicted_class]: float(confidence)}
 
 # Create a Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.inputs.Image(type="numpy", label="Upload an image"),
-    outputs=gr.outputs.Label(num_top_classes=3, label="Predictions"),
-    title="Pokémon Classifier",
-    description="Upload an image of a Pokémon and get the predicted class."
-)
+input_image = gr.Image()
+output_text = gr.Textbox(label="Predicted Value")
+interface = gr.Interface(fn=predict, 
+                         inputs=input_image, 
+                         outputs=gr.Label(),
+                         examples=["images/00000000.jpg", "images/00000001.jpg", "images/00000010.png", "images/00000017.jpg", "images/00000021.jpg", "images/00000067.jpg"],   
+                         description="A simple mlp classification model for image classification using the mnist dataset.")
 
 # Launch the Gradio interface
-iface.launch()
+interface.launch()