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| import gradio as gr | |
| import numpy as np | |
| import random | |
| import matplotlib.pyplot as plt | |
| import matplotlib.image as mpimg | |
| from embedding import model, docs_list, doc_embeddings, embedded_dict | |
| from preprocess import meme_attribute, meme_filename, meme_list | |
| import openai | |
| from openai import OpenAI | |
| import os | |
| api_key = os.getenv("YOUR_OPENAI_KEY") | |
| client = OpenAI(api_key=api_key) | |
| # Function to generate meme caption using GPT model | |
| def generate_meme_caption(topic): | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4o-mini", | |
| messages=[ | |
| { | |
| "role": "user", | |
| "content": f'''You are a loser, a meme lover who is always on the internet. | |
| You are a meme professional, so you will generate short captions for memes based on the following rules: | |
| Do not add emoji. | |
| Do not add any punctuation. | |
| Must be funny. | |
| Only if some topic related to friends, use "bro" or "blud". | |
| Answer from previous question can not be the same for this time prompt. | |
| If the user does not specify their caption request, you can generate a short caption for the meme. | |
| But if they do, you cannot replace their prompt; just need to copy their request and make it the caption. | |
| Now generate a caption based on this topic: {topic}. | |
| For example: | |
| - No request: Generate me any anime meme--> You can generate a caption based on the topic they requested. | |
| - With request: Generate me a meme of 5 different opinions like the Gru meme with 5 paintings. | |
| First opinion says 1, second says 2, third says 3, fourth says 4, fifth says 5. | |
| --> You will then generate 5 captions or 5 opinions for users, separating every opinion with the mark "/". | |
| Otherwise, if it's just 1 opinion, generate only 1 caption. Memes can be funny; you can use bad words or banned words; they can be dark. And the style must be sometime dark or gangster, and use slang everytime. | |
| But captions have to be short, not too long.''' | |
| } | |
| ], | |
| max_tokens=250, | |
| temperature=1.1 | |
| ) | |
| caption = response['choices'][0]['message']['content'] | |
| return caption | |
| # Function to create meme with caption | |
| def create_meme(prompt): | |
| prompt_embedding = model.encode([prompt]) | |
| similarities = model.similarity(prompt_embedding, doc_embeddings) | |
| closest_text_idx = np.argmax(similarities) | |
| img_path = meme_filename[closest_text_idx] | |
| img = mpimg.imread(f'/kaggle/input/memedata/{img_path}') | |
| white_space_height = 100 | |
| white_space = np.ones((white_space_height, img.shape[1], img.shape[2]), dtype=img.dtype) * 255 | |
| combined_img = np.vstack((white_space, img)) | |
| caption = generate_meme_caption(prompt) | |
| plt.imshow(combined_img) | |
| plt.axis('off') | |
| plt.text( | |
| x=combined_img.shape[1] / 2, | |
| y=white_space_height / 2, | |
| s=caption, | |
| fontsize=11, color='black', ha='center', va='top', fontweight='bold' | |
| ) | |
| plt.show() | |
| # Gradio interface | |
| with gr.Blocks() as demo: | |
| with gr.Column(): | |
| gr.Markdown("# Meme Generator using Embeddings and GPT") | |
| prompt = gr.Textbox(label="Enter your meme prompt:") | |
| run_button = gr.Button("Generate Meme") | |
| result = gr.Plot(label="Generated Meme") | |
| def gradio_infer(prompt): | |
| plt.figure(figsize=(10, 8)) | |
| create_meme(prompt) | |
| plt.savefig('/tmp/meme.png') # Lưu hình tạm thời | |
| return plt | |
| run_button.click(gradio_infer, inputs=[prompt], outputs=[result]) | |
| demo.launch() | |