from huggingface_hub import HfApi
import gradio as gr
from urllib.parse import urlparse
import requests
import time
import os

from utils.gradio_helpers import parse_outputs, process_outputs

inputs = []
inputs.append(gr.Textbox(
    label="Model Image", info='''Clear picture of the model'''
))

inputs.append(gr.Textbox(
    label="Garment Image", info='''Clear picture of upper body garment'''
))

inputs.append(gr.Textbox(
    label="Person Mask", info='''Mask of the person's upper body'''
))

inputs.append(gr.Slider(
    label="Steps", info='''Inference steps''', value=20,
    minimum=1, maximum=40, step=1,
))

inputs.append(gr.Slider(
    label="Guidance Scale", info='''Guidance scale''', value=2,
    minimum=1, maximum=5
))

inputs.append(gr.Number(
    label="Seed", info='''Seed''', value=0
))

inputs.append(gr.Slider(
    label="Num Samples", info='''Number of samples''', value=1,
    minimum=1, maximum=4, step=1,
))

names = ['model_image', 'garment_image', 'person_mask', 'steps', 'guidance_scale', 'seed', 'num_samples']

outputs = []
outputs.append(gr.Image())
outputs.append(gr.Image())
outputs.append(gr.Image())
outputs.append(gr.Image())

expected_outputs = len(outputs)
def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)):
    headers = {'Content-Type': 'application/json'}

    payload = {"input": {}}

    # TODO: extract the Bearer access token from the request
    if not request:
        raise gr.Error("The submission failed!")

    print("Request headers dictionary:", request.headers)

    try:
        authorization = request.headers["Authorization"]
    except KeyError:
        raise gr.Error("Missing authorization in the headers")
    
    # Extract the token part from the authorization
    try:
        bearer, token = authorization.split(" ")
    except ValueError:
        raise gr.Error("Invalid format for Authorization header. It should be 'Bearer <token>'")

    try:
        hf_api = HfApi(token=token)
        userInfo = hf_api.whoami(token)
        if not userInfo:
            raise gr.Error("The provider API key is invalid!")
    except Exception as err:
        raise gr.Error("The provider API key is invalid!")
    
    base_url = "http://0.0.0.0:7860"
    for i, key in enumerate(names):
        value = args[i]
        if value and (os.path.exists(str(value))):
            value = f"{base_url}/file=" + value
        if value is not None and value != "":
            payload["input"][key] = value

    response = requests.post("http://0.0.0.0:5000/predictions", headers=headers, json=payload)
    
    if response.status_code == 201:
        follow_up_url = response.json()["urls"]["get"]
        response = requests.get(follow_up_url, headers=headers)
        while response.json()["status"] != "succeeded":
            if response.json()["status"] == "failed":
                raise gr.Error("The submission failed!")
            response = requests.get(follow_up_url, headers=headers)
            time.sleep(1)
    if response.status_code == 200:
        json_response = response.json()
        #If the output component is JSON return the entire output response 
        if(outputs[0].get_config()["name"] == "json"):
            return json_response["output"]
        predict_outputs = parse_outputs(json_response["output"])
        processed_outputs = process_outputs(predict_outputs)
        difference_outputs = expected_outputs - len(processed_outputs)
        # If less outputs than expected, hide the extra ones
        if difference_outputs > 0:
            extra_outputs = [gr.update(visible=False)] * difference_outputs
            processed_outputs.extend(extra_outputs)
        # If more outputs than expected, cap the outputs to the expected number
        elif difference_outputs < 0:
            processed_outputs = processed_outputs[:difference_outputs]
        
        return tuple(processed_outputs) if len(processed_outputs) > 1 else processed_outputs[0]
    else:
        if(response.status_code == 409):
            raise gr.Error(f"Sorry, the Cog image is still processing. Try again in a bit.")
        raise gr.Error(f"The submission failed! Error: {response.status_code}")

title = "Demo for oot_diffusion_with_mask cog image by jbilcke"
model_description = "Don't mind me :) this is just a fork of viktorfa/oot_diffusion_with_mask"

app = gr.Interface(
    fn=predict,
    inputs=inputs,
    outputs=outputs,
    title=title,
    description=model_description,
    allow_flagging="never",
)
app.launch()