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="Prompt", info='''Describe the subject. Include clothes and hairstyle for more consistency.'''
))

inputs.append(gr.Textbox(
    label="Negative Prompt", info='''Things you do not want to see in your image'''
))

inputs.append(gr.Image(
    label="Subject", type="filepath"
))

inputs.append(gr.Slider(
    label="Number Of Outputs", info='''The number of images to generate.''', value=3,
    minimum=1, maximum=20, step=1,
))

inputs.append(gr.Slider(
    label="Number Of Images Per Pose", info='''The number of images to generate for each pose.''', value=1,
    minimum=1, maximum=4, step=1,
))

inputs.append(gr.Checkbox(
    label="Randomise Poses", info='''Randomise the poses used.''', value=True
))

inputs.append(gr.Dropdown(
    choices=['webp', 'jpg', 'png'], label="output_format", info='''Format of the output images''', value="webp"
))

inputs.append(gr.Number(
    label="Output Quality", info='''Quality of the output images, from 0 to 100. 100 is best quality, 0 is lowest quality.''', value=80
))

inputs.append(gr.Number(
    label="Seed", info='''Set a seed for reproducibility. Random by default.''', value=None
))

names = ['prompt', 'negative_prompt', 'subject', 'number_of_outputs', 'number_of_images_per_pose', 'randomise_poses', 'output_format', 'output_quality', 'seed']

outputs = []
outputs.append(gr.Image())
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": {}}
    
    
    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 consistent-character cog image by fofr"
model_description = "Create images of a given character in different poses"

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