import gradio as gr
import cv2
import numpy as np
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
import base64
import requests
from gradio_client import Client

# Load the segmentation model
sam_checkpoint = "sam_vit_h_4b8939.pth"
model_type = "vit_h"
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)

# Define a function for image segmentation
def segment_image(input_image):
    image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
    mask_generator = SamAutomaticMaskGenerator(sam)
    masks = mask_generator.generate(image)

    segmented_regions = []  # List to store segmented regions

    for i, mask_dict in enumerate(masks):
        mask_data = (mask_dict['segmentation'] * 255).astype(np.uint8)
        segmented_region = cv2.bitwise_and(input_image, input_image, mask=mask_data)

        x, y, w, h = map(int, mask_dict['bbox'])
        cropped_region = segmented_region[y:y+h, x:x+w]

        # Convert to base64 image
        _, buffer = cv2.imencode(".png", cv2.cvtColor(cropped_region, cv2.COLOR_BGR2RGB))
        segmented_image_base64 = base64.b64encode(buffer).decode()
        segmented_regions.append(segmented_image_base64)  # Add to the list

    return segmented_regions

# Function to call the API and calculate cosine similarity
def calculate_cosine_similarity(segmented_images):
    highest_cosine = -1
    highest_cosine_base64 = ""
    
    client = Client("https://ktllc-clip-model-inputbase64.hf.space/--replicas/mmz7z/")
    
    for base64_image in segmented_images:
        # Call the API here using the base64 image
        result = client.predict(base64_image, base64_image, api_name="/predict")
        
        cosine_value = result['similarity']
        print(f"Base64 Image: {base64_image}, Cosine Similarity: {cosine_value}")
            
        if cosine_value > highest_cosine:
            highest_cosine = cosine_value
            highest_cosine_base64 = base64_image

    print(f"Highest Cosine Similarity: {highest_cosine} (Base64 Image: {highest_cosine_base64})")

# Create Gradio components
input_image = gr.inputs.Image()
output_images = gr.outputs.JSON()

# Create a Gradio interface
segmentation_interface = gr.Interface(fn=segment_image, inputs=input_image, outputs=output_images)

# Launch the segmentation interface
segmentation_interface.launch()

# Get the segmented images from the segmentation interface
segmented_images = segmentation_interface.run()
segmentation_interface.close()

# Call the API for each segmented image and calculate cosine similarity
calculate_cosine_similarity(segmented_images)