app.py

# filename: app.py (Modified for consistent dummy output)

import gradio as gr
import PIL.Image
# No need for 'random' if you want consistent output
# import random

# This is a dummy function that simulates an image classification model.
# It now returns a consistent result for any image.
def classify_image(image: PIL.Image.Image) -> str:
    """
    Simulates classifying an input image and returns a consistent label.
    Args:
        image (PIL.Image.Image): The input image to classify.
    Returns:
        str: A consistent simulated classification label.
    """
    if image is None:
        return "No image provided."

    # In a real scenario, this is where your ML model would process the image
    # and return a real prediction.
    # For now, let's return a consistent dummy result.
    return "Classified as: Heart (Confidence: 0.92)" # Consistent output

# Create the Gradio Interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil", label="Upload an Image"),
    outputs=gr.Textbox(label="Classification Result"),
    title="Simple Image Classifier (Consistent Dummy)",
    description="Upload an image and get a consistent simulated classification result."
)

if __name__ == "__main__":
    print("Launching Gradio app locally on http://127.0.0.1:7860")
    iface.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False
    )