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| import gradio as gr | |
| from transformers import pipeline | |
| from glob import glob | |
| from PIL import Image | |
| import os | |
| from icrawler.builtin import GoogleImageCrawler | |
| def download_image(query, out_file): | |
| google_crawler = GoogleImageCrawler(storage={'root_dir': './'}) | |
| google_crawler.crawl(keyword=query, max_num=1, overwrite=True) | |
| os.rename(glob("./000001.*")[0], out_file) | |
| def generate_story(prompt): | |
| story = storygen(f"{prompt}")[0]['generated_text'] | |
| return story | |
| def start_neural_style_transfer(img1_name, img2_name): | |
| img1_filename, img2_filename = "style_transfer_1.jpg", "style_transfer_2.jpg" | |
| download_image(img1_name, img1_filename) | |
| download_image(img2_name, img2_filename) | |
| styled_image = nst(img1_filename, img2_filename) | |
| pil_img = Image.open(styled_image) | |
| return pil_img | |
| def detect_objects(file_name): | |
| out_img = detectron(file_name) | |
| pil_img = Image.open(out_img) | |
| return pil_img | |
| def main(text_input): | |
| text_output, image_output, metadata = None, None, None | |
| task_type_q = f"User: {text_input}\nWhat is the task the user is asking to do?\n \ | |
| - story generation task\n \ | |
| - image style transfer task\n \ | |
| - object detection task" | |
| task = t0pp(task_type_q) | |
| task = task.lower().replace('.', '') | |
| if task=="story generation task": | |
| story_prompt = t0pp(f"User: {text_input}\nWhat story is the user asking for?") | |
| text_output = generate_story(story_prompt) | |
| metadata = f"Prompt used to generate the story:\n{story_prompt}" | |
| elif task=="image style transfer task": | |
| img1_name = t0pp(f"User: {text_input}\nWhat is the name of the picture to which style is to be tranferred?") | |
| img2_name = t0pp(f"User: {text_input}\nWhat is the name of the picture from which style is to be tranferred?") | |
| image_output = start_neural_style_transfer(img1_name, img2_name) | |
| metadata = f"Image from which style is to be transferred: {img2_name}\nImage to which style is to be transferred: {img1_name}" | |
| elif task=="object detection task": | |
| img_file = "object_detection.jpg" | |
| img_name = t0pp(f"User: {text_input}\nWhat image is the user referring to?") | |
| download_image(img_name, img_file) | |
| image_output = detect_objects(img_file) | |
| metadata = f"Image from which objects are to be detected: {img_name}" | |
| return text_output, image_output | |
| if __name__=="__main__": | |
| t0pp = gr.Interface.load("huggingface/bigscience/T0pp") | |
| storygen = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2") | |
| nst = gr.Interface.load("spaces/luca-martial/neural-style-transfer") | |
| detectron = gr.Interface.load("spaces/akhaliq/Detectron2") | |
| gr.Interface( | |
| main, | |
| inputs=gr.inputs.Textbox(lines=5, label="Input"), | |
| outputs=[gr.outputs.Textbox(label="Output"), gr.outputs.Image(label="Ouptut"),] | |
| ).launch(debug=True) |