import s23_openai_clip from s23_openai_clip import make_train_valid_dfs from s23_openai_clip import get_image_embeddings from s23_openai_clip import inference_CLIP import gradio as gr import zipfile import os # query_text = "dogs on the grass" image_path = "./Images" captions_path = "." data_source = 'flickr8k.zip' print("\n\n") print("Going to unzip dataset") with zipfile.ZipFile(data_source, 'r') as zip_ref: zip_ref.extractall('.') print("unzip of dataset is done") print("Going to find captions.csv") find_txt_home = os.system("find /home/user/ -name captions.csv") find_txt_usr = os.system("find /usr/ -name captions.csv") print(find_txt_home) print(find_txt_usr) #============================================= import subprocess # shell command to run cmd = "ls -l" output1 = subprocess.check_output(cmd, shell=True).decode("utf-8") print(output1) cmd = "ls Images" output1 = subprocess.check_output(cmd, shell=True).decode("utf-8") print(output1) cmd = "pwd" output1 = subprocess.check_output(cmd, shell=True).decode("utf-8") print(output1) #============================================= print("Going to invoke make_train_valid_dfs") print("\n\n") _, valid_df = make_train_valid_dfs() model, image_embeddings = get_image_embeddings(valid_df, "best.pt") def greet(query_text): return inference_CLIP(query_text) gallery = gr.Gallery( label="Generated images", show_label=True, elem_id="gallery", columns=[3], rows=[3], object_fit="contain", height="auto") # btn = gr.Button("Generate images", scale=0) demo = gr.Interface(fn=greet, inputs="text", outputs=gallery) demo.launch("debug")