# import gradio as gr
# from segmentation import segment_image
# import numpy as np
# import cv2

# # Image de test par défaut
# default_image_path = "./image.png"

# def segment_and_display(image_path=default_image_path):
#     # Appeler la fonction de segmentation
#     original_image, segmented_image = segment_image(image_path)
    
#     # Retourner les images pour l'affichage
#     return original_image, segmented_image

# # Charger l'image de test par défaut
# default_original_image, default_segmented_image = segment_image(default_image_path)

# # Interface Gradio
# iface = gr.Interface(
#     fn=segment_and_display,
#     inputs=gr.Image(type="filepath", label="Upload Image"),
#     outputs=[
#         gr.Image(type="numpy", label="Original Image"),
#         gr.Image(type="numpy", label="Segmented Image")
#     ],
#     title="Image Segmentation with K-means (k=2)",
#     description="Upload an image or use the default test image to see the segmentation result.",
#     examples=[
#         [default_image_path]
#     ],
#     live=True  # Permet de voir les changements en temps réel
# )

# # Afficher l'image de test par défaut lorsque l'interface est ouverte
# iface.launch(share=True, inline=True)
import gradio as gr
from segmentation import segment_image
from medgemma_api import query_medgemma
import os

# 默认图片路径
default_image_path = "./image.png"

def segment_only(image_path):
    _, segmented_image = segment_image(image_path)
    return segmented_image

def analyze_with_medgemma(image, question):
    return query_medgemma(image, question)

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=1):
            image_input = gr.Image(type="filepath", label="Upload Image")
            segmented_output = gr.Image(type="numpy", label="Segmented Image")

            image_input.change(fn=segment_only, inputs=image_input, outputs=segmented_output)

        with gr.Column(scale=2):
            chatbot = gr.Textbox(label="Ask MedGemma", placeholder="Enter your medical question...")
            image_for_analysis = gr.Image(type="filepath", label="Upload image for analysis (optional)")
            analyze_button = gr.Button("Analyze")
            response_output = gr.Textbox(label="Response")

            analyze_button.click(fn=analyze_with_medgemma, inputs=[image_for_analysis, chatbot], outputs=response_output)

demo.launch()