Create app.py

app.py

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+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
+
+# 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 JSON format with the keys being 'primary', 'headline', and 'description'. Be strict about this format. It will be read as a JSON file. 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),
+        }
+    )
+
+    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
+    )
+    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)
+    responses = json.loads(responses)
+
+    dall_e_prompt = promptImg + " , " + lens_option
+    primary = responses["primary"]
+    headline = responses["headline"]
+    description = responses["description"]
+    # 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()