import transformers
import torch
import gradio as gr
import requests

from transformers import BlipForImageTextRetrieval
from transformers import AutoProcessor
from transformers.utils import logging
from PIL import Image
logging.set_verbosity_error()

model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco")
processor = AutoProcessor.from_pretrained("Salesforce/blip-itm-base-coco")

def process_image(input_type, image_url, image_upload, text):
    if input_type == "URL":
        raw_image =  Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
    else:
        raw_image = image_upload

    inputs = processor(images=raw_image, text=text, return_tensors="pt")
    itm_scores = model(**inputs)[0]
    itm_score = torch.nn.functional.softmax(itm_scores,dim=1)
    itm_score = itm_score[0][1]
    print(itm_score)

    if itm_score <=.35:
        cmnt = "which is not that great. Try again."
    elif itm_score <= .75:
        cmnt = "which is good. But you can improve it. Try again."
    elif itm_score == 1.0:
        cmnt = "and that is an unbelievable perfect score. You have achieved the near impossible. Congratulations"
    else:
        cmnt = "which is excellent. Can you improve on it?"

    formatted_text = (
        f"""<div><h1 style='text-align: center; font-size: 80px; color: blue;'>
        Your decription score is <span style='font-size: 100px; color: orange;'>{itm_score*100:.2f}/100</span> {cmnt}
        </h1></div>"""
    )
    return formatted_text

def display_image_from_url(image_url):
    if image_url:
        image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
        return image
    return None

def toggle_inputs(input_type):
    if input_type == "URL":
        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)
    else:
        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)

with gr.Blocks() as demo:
    gr.Markdown(
    """
    # Challenge yourself by describing the image - test & demo app by Srinivas.V..
    Paste either URL of an image or upload the image, describe the image best and submit to know your score.
    """)
    
    input_type = gr.Radio(choices=["URL", "Upload"], label="Input Type")
    image_url = gr.Textbox(label="Image URL", visible=False)
    url_image = gr.Image(type="pil", label="URL Image", visible=False)
    image_upload = gr.Image(type="pil", label="Upload Image", visible=False)
    description = gr.Textbox(label="Describe the image", visible=False, lines=3)
    
    input_type.change(fn=toggle_inputs, inputs=input_type, outputs=[image_url, url_image, image_upload, description])
    image_url.change(fn=display_image_from_url, inputs=image_url, outputs=url_image)

    submit_btn = gr.Button("Submit")
    processed_image = gr.HTML(label="Your challenge result")
    submit_btn.click(fn=process_image, inputs=[input_type, image_url, image_upload, description], outputs=processed_image)

demo.launch(debug=True, share=True)