app.py

import gradio as gr
from gradio_client import Client, handle_file
from PIL import Image
import tempfile

# --------------------------------------------------------
# API Clients
# --------------------------------------------------------
det_client = Client("Korbd/object_detection_model")
cls_client = Client("Korbd/image_classification_model")


# --------------------------------------------------------
# Main Prediction Function
# --------------------------------------------------------
def run_prediction(image, task, threshold):

    if image is None:
        return None, None

    # Save to temp file (same as your Streamlit version)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
        Image.fromarray(image).save(tmp.name)
        tmp_path = tmp.name

    classification_result = None
    detection_result = None

    # ---- Classification ----
    if task in ["Classification", "Both"]:
        try:
            classification_result = cls_client.predict(
                image=handle_file(tmp_path),
                api_name="/predict"
            )
        except Exception as e:
            classification_result = {"error": str(e)}

    # ---- Object Detection ----
    if task in ["Object Detection", "Both"]:
        try:
            detection_result = det_client.predict(
                image=handle_file(tmp_path),
                api_name="/detect_objects"
            )
        except Exception as e:
            detection_result = {"error": str(e)}

    return classification_result, detection_result


# --------------------------------------------------------
# UI
# --------------------------------------------------------
with gr.Blocks(title="Image Predictor") as demo:

    gr.Markdown(
        """
        # 📸 Image Predictor — Classification & Detection
        ---
        """
    )

    with gr.Row():
        with gr.Column(scale=1):
            image_input = gr.Image(
                type="numpy",
                label="Upload an Image"
            )

            task = gr.Radio(
                ["Classification", "Object Detection", "Both"],
                value="Both",
                label="Task",
            )

            threshold = gr.Slider(
                0.0, 1.0, 0.5,
                step=0.01,
                label="Detection Score Threshold"
            )

            run_button = gr.Button("Run Prediction", variant="primary")

        with gr.Column(scale=1):
            class_output = gr.JSON(label="🔵 Classification Result")
            detect_output = gr.JSON(label="🟠 Object Detection Result")

    run_button.click(
        fn=run_prediction,
        inputs=[image_input, task, threshold],
        outputs=[class_output, detect_output]
    )


demo.launch()