from turtle import title
import os 
import gradio as gr
from transformers import pipeline
import numpy as np
from PIL import Image
import torch 
import cv2 
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation,AutoProcessor,AutoConfig
from skimage.measure import label, regionprops

processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
classes = list()

def rescale_bbox(bbox,orig_image_shape=(1024,1024),model_shape=352):
    bbox = np.asarray(bbox)/model_shape
    y1,y2 = bbox[::2] *orig_image_shape[0]
    x1,x2 = bbox[1::2]*orig_image_shape[1]
    return [int(y1),int(x1),int(y2),int(x2)]

def detect_using_clip(image,prompts=[],threshould=0.4):
    model_detections = dict()
    inputs = processor(
        text=prompts,
        images=[image] * len(prompts),
        padding="max_length",
        return_tensors="pt",
    )
    with torch.no_grad():  # Use 'torch.no_grad()' to disable gradient computation
        outputs = model(**inputs)
    preds = outputs.logits.unsqueeze(1)
    detection = outputs.logits[0]  # Assuming class index 0
    for i,prompt in enumerate(prompts):
        predicted_image =  torch.sigmoid(preds[i][0]).detach().cpu().numpy()
        predicted_image = np.where(predicted_image>threshould,255,0)
        # extract countours from the image
        lbl_0 = label(predicted_image)
        props = regionprops(lbl_0)
        model_detections[prompt] = [rescale_bbox(prop.bbox,orig_image_shape=image.shape[:2],model_shape=predicted_image.shape[0]) for prop in props]

    return model_detections

def display_images(image,detections,prompt='traffic light'):
    H,W = image.shape[:2]
    image_copy = image.copy()
    if prompt not in detections.keys():
        print("prompt not in query ..")
        return image_copy
    for bbox in detections[prompt]:
        cv2.rectangle(image_copy, (int(bbox[1]), int(bbox[0])), (int(bbox[3]), int(bbox[2])), (255, 0, 0), 2)
    return image_copy


def shot(image, labels_text,selected_categoty):
    print("Labels Text ",labels_text)
    prompts = labels_text.split(',')
    classes = prompts

    print("prompts :",prompts,classes)
    print("Image shape ",image.shape )

    detections  = detect_using_clip(image,prompts=prompts)
    print("detections :",detections)
    print("Ctegory ",selected_categoty)
    return 0

iface = gr.Interface(fn=shot,
                    inputs = ["image","text",gr.Dropdown(classes, label="Category Label",info='Select Categories')],
                    outputs="image",
                    description="Add a picture and a list of labels separated by commas",
                    title="Zero-shot Image Classification with Prompt ",
                    examples=[["images/room.jpg","bed,table,plant"]],
                    # allow_flagging=False, 
                    # analytics_enabled=False,
                )
iface.launch()