import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import json
import torch
import requests
import time
import random
from PIL import Image
from typing import Union
import os
import base64

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using {device}" if device != "cpu" else "Using CPU")

def _load_model():
    tokenizer = AutoTokenizer.from_pretrained("vikhyatk/moondream2", trust_remote_code=True, revision="2024-05-08", torch_dtype=(torch.bfloat16 if device == 'cuda' else torch.float32))
    model = AutoModelForCausalLM.from_pretrained("vikhyatk/moondream2", device_map=device, trust_remote_code=True, revision="2024-05-08")
    return (model, tokenizer)

class MoonDream():
    def __init__(self, model=None, tokenizer=None):
        self.model, self.tokenizer = (model, tokenizer)
        if not model or model is None or not tokenizer or tokenizer is None:
            self.model, self.tokenizer = _load_model()
        self.device = device
        self.model.to(self.device)
    def __call__(self, question, imgs):
        imn = 0
        for img in imgs:
            img = self.model.encode_image(img)
            res = self.model.answer_question(question=question, image_embeds=img, tokenizer=self.tokenizer)
            yield res
        return

md = MoonDream()

def _respond_one(question, img):
  txt = ""
  yield (txt := txt + MoonDream()(question, [img]))
  return txt

def respond_batch(question, **imgs):
  md = MoonDream()
  for img in imgs.values():
    res = md(question, img)
    for r in res:
      yield r
    yield "\n\n\n\n\n\n"
  return

def dual_images(img1: Image):
  # Ran once for each img to it's respective output. Output should be detailed str of description/feature extraction/interrogation.
  md = MoonDream()
  res = md("Describe the image in plain english ", [img1])
  txt = ""
  for r in res:
    yield (txt := txt + r)
  return

import os

def merge_descriptions_to_prompt(mi, d1, d2):
  from together import Together
  tog = Together(api_key=os.getenv("TOGETHER_KEY"))
  res = tog.completions.create(prompt=f"""Describe what would result if the following two descriptions were describing one thing.
### Description 1:
```text
{d1}
```
### Description 2:
```text
{d2}
```
Merge-Specific Instructions:
```text
{mi}
```
Ensure you end your output with ```\\n
---
Complete Description:
```text""", model="meta-llama/Meta-Llama-3-70B", stop=["```"], max_tokens=1024)
  return res.choices[0].text.split("```")[0]

def xform_image_description(img, inst):
  #md = MoonDream()
  from together import Together
  desc = dual_images(img)
  tog = Together(api_key=os.getenv("TOGETHER_KEY"))
  prompt=f"""Describe the image in aggressively verbose detail. I must know every freckle upon a man's brow and each blade of the grass intimately.\nDescription: ```text\n{desc}\n```\nInstructions:\n```text\n{inst}\n```\n\n\n---\nDetailed Description:\n```text"""
  res = tog.completions.create(prompt=prompt, model="meta-llama/Meta-Llama-3-70B", stop=["```"], max_tokens=1024)
  return res.choices[0].text[len(prompt):].split("```")[0]

def simple_desc(img, prompt):
  gen = md(prompt, [img])
  total = ""
  for resp in gen:
    print(total := total + resp)
  img.resize((102,192)).save("tmp.png")
  bts = False
  with open("tmp.png", "rb") as f:
    bts = f.read()
  if bts:
    os.remove("tmp.png")
  res = {
    'image_b64': base64.b64encode(bts).decode('utf-8'),
    'description': total,
  }
  return total, res

ifc_imgprompt2text = gr.Interface(simple_desc, inputs=[gr.Image(label="input", type="pil"), gr.Textbox(label="prompt")], outputs=[gr.Textbox(label="description"), gr.JSON(label="json")])


"""
with gr.Blocks() as demo:
  
  with gr.Row():
    with gr.Column():
      im1 = gr.Image(label="image 1", type='pil')
      otp2 = gr.Textbox(label="image 1", interactive=True)
    with gr.Column():
      im2 = gr.Image(label="image 2", type='pil')
      otp3 = gr.Textbox(label="image 2")
  with gr.Row():
    minst = gr.Textbox(label="Merge Instructions")
  with gr.Row():
    btn2 = gr.Button("submit batch")
  with gr.Row():
    with gr.Column():
      im1 = gr.Image(label="image 1", type='pil')
      otp2 = gr.Textbox(label="individual batch output (left)", interactive=True)
    with gr.Column():
      im2 = gr.Image(label="image 2", type='pil')
      otp3 = gr.Textbox(label="individual batch output (right)", interactive=True)
  with gr.Row():
    otp4 = gr.Textbox(label="batch output ( combined )", interactive=True, lines=4)
  with gr.Row():
    btn_scd = gr.Button("Merge Descriptions to Single Combined Description")
  btn2.click(dual_images, inputs=[im1], outputs=[otp2])
  btn2.click(dual_images, inputs=[im2], outputs=[otp3])
  btn.click(dual_images, inputs=[img], outputs=[otpt])
  btn_scd.click(merge_descriptions_to_prompt, inputs=[minst, otp2, otp3], outputs=[otp4])

  demo.launch(debug=True, share=True)
  """
with gr.TabbedInterface([ifc_imgprompt2text], ["Prompt & Image 2 Text"]) as ifc:
  ifc.launch(share=False)