app.py

import openai
import io
import os
import matplotlib.pyplot as plt
from PIL import Image
import requests
from io import BytesIO
import base64
from IPython.display import display, clear_output, Image as IPyImage
import json
import gradio as gr
import PIL.Image
import io
import numpy as np
#All abstractions provided by langchain
from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate
from dotenv import load_dotenv
import sys
import os
from io import StringIO
import re

# Load environment variables from .env file
load_dotenv()

template = """Assistant is a large language model trained by OpenAI.

Assistant is designed to generate catchy texts for Facebook Ads post. Assistant will receive some details about a product such as type, color, price, and potential customers. 

Based on these parameters it will generate the following 4 fields - 

primary: max 125 characters  

headline: max 27 characters 

description: max 27 characters  

Each field should be totally different with minimal repetition. Do not repeatedly use phrases that sound to sales like. Output your responses as a string with the keys being 'primary', 'headline', and 'description' and getting a new Line after each key. Be strict about this format. Just directly output responses nothing else, no leading words, etc.

Consider this product : {human_input}
"""

prompt = PromptTemplate(
    input_variables=["human_input"], 
    template=template
)

chatgpt_chain = LLMChain(
    llm=OpenAI(temperature=1), 
    prompt=prompt, 
    verbose=True, 
)

#Helper function as this is what Pebblely API takes in
# def image_to_base64(img_path):
#     with open(img_path, "rb") as image_file:
#         return base64.b64encode(image_file.read()).decode('utf-8')

# #Removes backdrop from image using Pebblely API
# def removeBackdrop(image_path, output_path):
#     endpoint_url = "https://api.pebblely.com/remove-background/v1/"

#     response = requests.post(
#         endpoint_url,
#         headers={
#             "Content-Type": "application/json",
#             "X-Pebblely-Access-Token": os.environ["PEBBLELY_TOKEN"]
#         },
#         json={
#             "image": image_to_base64(image_path),
#         }
#     )
#     print(response)
#     image_b64 = response.json()["data"]
#     image_encoded = image_b64.encode("utf-8")
#     image_bytes = io.BytesIO(base64.b64decode(image_encoded))
#     image = Image.open(image_bytes)
#     image.save(output_path)

#Takes the images from Pebblely output and generates images with different backdrops based on user prompts
# def DallEBackdropGen(image_path, prompt, n_img, size):
#     response = openai.Image.create_edit(
#       image=open(image_path, "rb"),
#       prompt=prompt,
#       n=n_img,
#       size=size
#     )
#     print(type(response))
#     print(response)
#     return response['data']

#Just outputs the images from DallE in a nice grid for easy consumption
# def outputGenImages(ori_image, response_data):
#     nrows = len(response_data)
#     # List of image URLs
#     image_urls = [response_data[i]['url'] for i in range(nrows)]

#     # Load all the images into a list
#     images = [Image.open(BytesIO(requests.get(url).content)) for url in image_urls]

#     # Create a new image of the right size
#     width, height = images[0].size  # assumes all images are the same size
#     total_height = height * nrows
#     new_im = Image.new('RGB', (width, total_height))

#     # Paste in each image
#     for i, img in enumerate(images):
#         new_im.paste(img, (0, i * height))

#     return new_im

def upload_and_process(image, promptText, promptImg, lens_option, n_images, size):
    # We are assuming the removeBackdrop, DallEBackdropGen, outputGenImages are defined and available in your code
    # image_path = "temp.png"
    # image.save(image_path)
    # Remove backdrop
    responses = chatgpt_chain.predict(human_input=promptText)
    print(responses)
    index = responses.find("Output:")

    # Remove "Output:" from the string if found
    if index != -1:
        responses = responses[:index] + responses[index + len("Output:"):]
    split_pairs = responses.split(" ")
    pk =""
    hk =""
    dk = ""
    primary = ""
    headline = ""
    description = ""
    
    # Initialize an empty dictionary to store the key-value pairs
    key_value_dict = {}
    key = None
    value = ""
    for pair in split_pairs:
        if ":" in pair:
            if key is not None:
                key_value_dict[key.strip()] = value.strip()
            key, value = pair.split(":", 1)
        else:
            value += " " + pair.strip()
    
    # Add the last key-value pair
    if key is not None:
        key_value_dict[key.strip()] = value.strip()
    
    # Print each key-value pair on a new line
    for key, value in key_value_dict.items():
        print(key + ": " + value)
    items = list(key_value_dict.items())
    if items[0][0] in'primary':
        primary=items[0][1]
    elif items[0][0] in 'headline':
        headline=items[0][1]
    elif items[0][0] in 'description':
        description=items[0][1]
    
    
    # json_string = responses.strip()

    # # Add indentation and new lines after each key-value pair
    # formatted_json = ''
    # indentation_level = 0
    # for char in json_string:
    #     if char == '{' or char == '[':
    #         formatted_json += char + '\n' + '    ' * indentation_level
    #         indentation_level += 1
    #     elif char == '}' or char == ']':
    #         indentation_level -= 1
    #         formatted_json += '\n' + '    ' * indentation_level + char
    #     elif char == ',':
    #         formatted_json += char + '\n' + '    ' * indentation_level
    #     else:
    #         formatted_json += char

    # print(formatted_json)
    # dall_e_prompt = promptImg + " , " + lens_option
    # # Generate variations using DALL-E
    # removeBackdrop(image_path, "temp_no_bg.png")
    # NoBackImg = "temp_no_bg.png"
    # response_data = DallEBackdropGen("temp_no_bg.png", dall_e_prompt, n_images, size)
    
    # # Get and return the images
    # result_images = outputGenImages(image_path, response_data)
    
    text_output = f"<h2>{headline}</h2><h3>{primary}</h3><p>{description}</p>"
    
    return text_output#, result_images  # Output should be a list of PIL Image objects or numpy arrays

lens_options = [
    "Sigma 85 mm f/1.4 (good for portrait)", 
    "Sigma 85 mm f/8(sharper background)", 
    "Sigma 24 mm f/8 (wider angle)",
    "Nikon D810 | ISO 64 | focal length 20 mm (Voigtländer 20 mm f3.5) | aperture f/9 | exposure time 1/40 Sec (DRI)",
    "Canon EOS 1000D, ƒ/3.5, focal length: 18.0 mm, exposure time: 1/5, ISO 400"
]

iface = gr.Interface(
    fn=upload_and_process, 
    inputs=[
        gr.inputs.Image(type="pil", label="Upload Image", image_mode='RGB', tool="editor", source="upload"), 
        gr.inputs.Textbox(default='Enter product name, price, target customer, etc.', label="Text Prompt"), 
        gr.inputs.Textbox(default='Enter the desired image to be generated', label="Image Prompt"),
        gr.inputs.Dropdown(choices=lens_options, label="Lens Option"),
        gr.inputs.Slider(minimum=1, maximum=10, default=4, step=1, label="No. of Images"),
        gr.inputs.Textbox(default='512x512', label="Size"),
    ],
    outputs=[
        gr.outputs.HTML(label="Generated Text"),
        # gr.outputs.Image(type="pil", label="Generated Images"),  # adjust the size as per your need
    ],
    title="Facebook Ad Creation",
)

iface.launch()