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import gradio as gr
from transformers import pipeline
from PIL import Image
image_class_pipe = pipeline(task="image-classification", model="google/vit-large-patch16-224")
video_class_pipe = pipeline(task="video-classification", model="nateraw/videomae-base-finetuned-ucf101-subset")
depth_estimator = pipeline(task="depth-estimation", model="Intel/dpt-large")
image_caption = pipeline("image-to-text",model="Salesforce/blip-image-captioning-base")
def classify_image_func(arr):
img = Image.fromarray(arr)
image_result = image_class_pipe(img)
return image_result[0]["label"]
def classify_video_func(vid):
video_result = video_class_pipe(vid)
return video_result
def estimate_depth_func(arr):
img = Image.fromarray(arr)
depth_result = depth_estimator(img)
return depth_result["depth"]
def blip_captioning_func(arr):
img = Image.fromarray(arr)
image_caption_result = image_caption(img, max_new_tokens=500)
return image_caption_result[0]["generated_text"]
with gr.Blocks() as demo:
gr.Markdown("# AI Methods")
with gr.Tab("Media Classification"):
gr.Markdown("# Image Classification")
with gr.Row():
classify_image_input = gr.Image(width=340, height=340)
with gr.Row():
classify_image_btn = gr.Button("Classify Image")
classify_image_output = gr.Textbox(label="Result")
classify_image_btn.click(fn=classify_image_func, inputs=[classify_image_input], outputs=[classify_image_output])
gr.Markdown("# Video Classification")
with gr.Row():
classify_video_input = gr.Video(width=340, height=340)
with gr.Row():
classify_video_btn = gr.Button("Classify Video")
classify_video_output = gr.Textbox(label="Result")
classify_video_btn.click(fn=classify_video_func, inputs=[classify_video_input], outputs=[classify_video_output])
with gr.Tab("Depth"):
gr.Markdown("# Depth Estimation")
with gr.Row():
depth_estimation_input = gr.Image(width=260, height=260)
with gr.Row():
depth_estimation_btn = gr.Button("Estimate Depth")
with gr.Row():
depth_estimation_output = gr.Image()
depth_estimation_btn.click(fn=estimate_depth_func, inputs=[depth_estimation_input], outputs=[depth_estimation_output])
with gr.Tab("BLIP Captioning"):
gr.Markdown("# BLIP Captioning")
with gr.Row():
blip_input = gr.Image(width=260, height=260)
with gr.Row():
blip_btn = gr.Button("BLIP Caption")
blip_output = gr.Textbox(label="Caption")
blip_btn.click(fn=blip_captioning_func, inputs=[blip_input], outputs=[blip_output])
demo.launch(debug=True) |