Update app.py

app.py

@@ -2,34 +2,34 @@ import gradio as gr
 import tensorflow as tf
 from PIL import Image
 import numpy as np
- 
-labels = ['barrel_jellyfish','blue_jellyfish','compass_jellyfish','lions_mane_jellyfish','mauve_stinger_jellyfish','Moon_jellyfish']
- 
-def predict_jellyfish_type(uploaded_file):
-    
-    if uploaded_file is None:
-        return "No file uploaded."
-   
-    model = tf.keras.models.load_model('Jellyfish_InceptionResNetV2_transferlearning.keras')
-    # Load the image from the file path
-    with Image.open(uploaded_file) as img:
-        img = img.resize((150, 150)).convert('RGB')  # Convert image to RGB
-        img_array = np.array(img)
- 
-        prediction = model.predict(np.expand_dims(img_array, axis=0))
-        confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
- 
-        return confidences
- 
- 
-# Define the Gradio interface
-iface = gr.Interface(
-    fn=predict_jellyfish_type,  # Function to process the input
-    inputs=gr.File(label="Upload File"),  # File upload widget
-    outputs="text",  # Output type
-    title="Jellyfish Classifier",  # Title of the interface
-    description="Upload a picture of a Jellyfish (preferably barrel, blue, compass, lions mane, mauve stinger, Moon)"  # Description of the interface
-)
- 
-# Launch the interface
-iface.launch()
+
+# Load your custom regression model
+model_path = "Jellyfish_InceptionResNetV2_transferlearning.weights.h5"
+model_path = "Jellyfish_InceptionResNetV2_transferlearning.keras"
+
+#model.load_weights(model_path)
+model = tf.keras.models.load_model(model_path)
+
+labels = ['barrel_jellyfish', 'blue_jellyfish', 'compass_jellyfish', 'lions_mane_jellyfish', 'mauve_stinger_jellyfish', 'Moon_jellyfish']
+
+# Define regression function
+def predict_regression(image):
+    # Preprocess image
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((28, 28)).convert('L') #resize the image to 28x28 and converts it to gray scale
+    image = np.array(image)
+    print(image.shape)
+    # Predict
+    prediction = model.predict(image[None, ...])  # Assuming single regression value
+    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
+    return confidences
+
+# Create Gradio interface
+input_image = gr.Image()
+output_text = gr.Textbox(label="Predicted Value")
+interface = gr.Interface(fn=predict_regression, 
+                         inputs=input_image, 
+                         outputs=gr.Label(),
+                         examples=["images/barrel_jellyfish.jpg", "images/blue_jellyfish.jpg", "images/compass_jellyfish.jpg", "images/lions_mane_jellyfish.jpg"],   
+                         description="A simple mlp classification model for image classification using the mnist dataset.")
+interface.launch()