Update app.py

app.py

@@ -1,12 +1,40 @@
 import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
 
-description = "Story generation with GPT-2"
-title = "Generate your own story"
-examples = [["Adventurer is approached by a mysterious stranger in the tavern for a new quest."]]
+# Load your custom regression model
+model_path = "freshwaterfish_classifier_model_ResNet50.keras"
+model = tf.keras.models.load_model(model_path)
 
-interface = gr.Interface.load("huggingface/pranavpsv/gpt2-genre-story-generator",
-            description=description,
-            examples=examples
-)
+labels = ['Catfish', 'Freshwater Eel', 'Goby', 'Perch']
 
+# Define regression function
+def predict_regression(image):
+    # Preprocess image
+    image = Image.fromarray(image.astype('uint8'))
+    image = image.resize((224, 224)).convert('RGB')
+    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
+
+# Define the file paths for the test images
+test_images = ["myImages/Egli1.jpeg", "myImages/Egli2.jpeg", "myImages/Wels1.jpeg", "myImages/Wels2.jpeg"]
+
+# Load and preprocess the test images
+example_images = [np.array(Image.open(image_path).resize((224, 224)).convert('RGB')) / 255.0 for image_path in test_images]
+
+# 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(),
+                         title="Freshwater Fish Classifier",
+                         description="This model predicts four species of a freshwater fish from the lake of constance.",
+                        examples=example_images
+                        )
 interface.launch()