import gradio as gr
from openai import OpenAI
import os
import requests
import time
from utils import uploadImages
import json

os.environ["OPENAI_API_KEY"] = "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"
placidAPIKey = "placid-vwkiebbjlxl9dnhl-t6c89f8b5c0q8b25"

client = OpenAI()

def createCampaign(template_uuid, title, details, file_url):
    try:
        response = requests.post(
            url="https://api.placid.app/api/rest/images",
            headers={
                "Authorization": f"Bearer {placidAPIKey}",
                "Content-Type": "application/json",
            },
            json = {
                "template_uuid": template_uuid,
                "layers": {
                    "title": {
                        "text": title
                    },
                    "description": {
                        "text": details
                    },
                    "screenshot": {
                        "image": file_url
                    }
                }
            }
        )
        
        response_data = response.json()
        print(response_data)

        polling_url = response_data['polling_url']        
        while True:
            # Make a GET request to the polling URL
            headers={
                "Authorization": f"Bearer {placidAPIKey}",
                "Content-Type": "application/json",
            }
            response = requests.get(polling_url, headers=headers)
            
            # Check if the request was successful
            if response.status_code == 200:
                data = response.json()
                
                # Check the status of the image processing
                if data['status'] == 'finished':
                    print("Processing finished!")
                    return data["image_url"]
                elif data['status'] == 'failed':
                    print("Processing failed.")
                    # Handle failure (you may also check data['errors'] for more details)
                    break
                else:
                    print("Still processing... Status:", data['status'])
                    time.sleep(2)  # Wait for 5 seconds before polling again
            else:
                print("Failed to poll status. HTTP status code:", response.status_code)
                break

    except requests.exceptions.RequestException:
        print('HTTP Request failed')


def predict(productInfo, screenshots):
    # Step 1: Get campaign copy from openAI
    system_ins = "You are a professional marketer, \
        if user enter a link then extract the information and use it as production context, \
        then create a campaign with 6 campaign items based on user input, each with a title and description.\
        Output the campaigns in json, it should only contain an array of the campaign items, no root key needed.\
        Don't add other info"

    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": system_ins},
            {"role": "user", "content": productInfo}
            ]
    )
    
    campaignCopy = response.choices[0].message.content
    print(campaignCopy)
    campaign_json = json.loads(campaignCopy)
    print(campaign_json)
    print(type(campaign_json))
    campaignItems = campaign_json
    
    print("Step 1: Complete")

    # Step 2: Upload all the images 
    file_urls = uploadImages(screenshots)
    print("Step 2: Complete")
    print(file_urls)

    # Step 3: Create the campain
    results = []
    templates = ["zzpwonwvxudb9", "qpoihnji5reqn", "jasfxwdgopiz5", "tsvhpyqygoq0b", "jinkqm8vixdpi"]
    for index, file_url in enumerate(file_urls):
        copy_title = campaignItems[index]['title']
        copy_des = campaignItems[index]['description']
        url = createCampaign(template_uuid=templates[index], title=copy_title, details=copy_des, file_url=file_url)

        results.append(url)

    print("Loading image from")
    print(results)

    return results

with gr.Blocks() as demo:
    with gr.Row():
        textBox = gr.Text(label="Enter your product info")
        imageBox = gr.Gallery(label="Upload Screenshots")
    with gr.Row():
        outputGallery = gr.Gallery(label="AppStore Screenshots")

    # Assuming a button is used to trigger the processing
    btn_process = gr.Button("Submit")

    # Function binding
    btn_process.click(predict, inputs=[textBox, imageBox], outputs=[outputGallery])

demo.launch()